# Reinforcement learning

Reinforcement learning is a machine learning approach in which agents learn to make decisions by interacting with an environment through trial and error and rewards.

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## PPO vs GRPO, Simply Explained

DevFeed: [PPO vs GRPO, Simply Explained](<https://devfeed.tech/articles/ppo-vs-grpo-simply-explained-41275.md>)

Original publisher: [Read original article](<https://www.intoai.pub/p/ppo-vs-grpo-simply-explained>)

Author: Dr. Ashish Bamania

Published: 2026-09-17T11:47:38Z

Content type: tutorial

Language: en

Sources: [Into AI](<https://devfeed.tech/sources/into-ai.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [post-training](<https://devfeed.tech/topics/post-training.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [alignment](<https://devfeed.tech/tags/alignment.md>), [human-feedback](<https://devfeed.tech/tags/human-feedback.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llm-training](<https://devfeed.tech/tags/llm-training.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

A tutorial comparing PPO and GRPO as reinforcement learning algorithms used in LLM post-training. It explains PPO, including RLHF, policy-gradient updates, and clipped token-probability changes intended to keep model behavior close to its previous version.

### Source excerpt

A simple lesson on two important LLM post-training algorithms.

## DACA-GRPO: Denoising-Aware Credit Assignment for Reinforcement Learning in Diffusion Language Models

DevFeed: [DACA-GRPO: Denoising-Aware Credit Assignment for Reinforcement Learning in Diffusion Language Models](<https://devfeed.tech/articles/daca-grpo-denoising-aware-credit-assignment-for-reinforcement-learning-in-diffusion-language-models-31489.md>)

Original publisher: [Read original article](<https://machinelearning.apple.com/research/denoising-aware-credit-assignment>)

Published: 2026-09-16T00:00:00Z

Content type: article

Language: en

Sources: [Apple Machine Learning Research](<https://devfeed.tech/sources/apple-machine-learning-research.md>)

Topics: [grpo](<https://devfeed.tech/topics/grpo.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [large-language-models](<https://devfeed.tech/topics/large-language-models.md>), [LLM Techniques](<https://devfeed.tech/topics/llm-techniques.md>), [Code](<https://devfeed.tech/topics/code.md>)

Tags: [code-generation](<https://devfeed.tech/tags/code-generation.md>), [denoising](<https://devfeed.tech/tags/denoising.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [grpo](<https://devfeed.tech/tags/grpo.md>), [json-schema](<https://devfeed.tech/tags/json-schema.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>)

### AI overview

The article introduces DACA-GRPO, a denoising-aware enhancement for GRPO-style reinforcement-learning trainers used with diffusion language models. It uses per-token denoising progress scores and stratified masking likelihood to improve credit assignment and reduce bias in likelihood estimates. Across seven benchmarks, the method reports gains in mathematical reasoning, code generation, constraint satisfaction, and JSON Schema adherence.

### Source excerpt

Diffusion large language models are a compelling alternative to autoregressive models, yet existing RL methods for diffusion treat all denoising steps as equally important and rely on biased, high-variance likelihood estimates. We identify two fundamental weaknesses: the absence of temporal credit assignment across the denoising trajectory, and the systematic bias of mean-field likelihood estimates used for policy optimization. To address these, we propose Denoising-Aware Credit Assignment for GRPO (DACA-GRPO), a lightweight, plug-and-play enhancement for any GRPO-style trainer. DACA-GRPO...

## Bypassing inference bottlenecks: Accelerating complex AI search with Retrieve-for-Train

DevFeed: [Bypassing inference bottlenecks: Accelerating complex AI search with Retrieve-for-Train](<https://devfeed.tech/articles/bypassing-inference-bottlenecks-accelerating-complex-ai-search-with-retrieve-for-train-26972.md>)

Original publisher: [Read original article](<https://research.google/blog/bypassing-inference-bottlenecks-accelerating-complex-ai-search-with-retrieve-for-train/>)

Published: 2026-09-15T20:00:35Z

Content type: article

Language: en

Sources: [The latest research from Google](<https://devfeed.tech/sources/the-latest-research-from-google.md>)

Topics: [AI search](<https://devfeed.tech/topics/ai-search.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Google](<https://devfeed.tech/topics/google.md>), [LLMs](<https://devfeed.tech/topics/llms.md>), [Algorithms & Theory](<https://devfeed.tech/topics/algorithms-theory.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-search](<https://devfeed.tech/tags/ai-search.md>), [algorithms-theory](<https://devfeed.tech/tags/algorithms-theory.md>), [data-mining-modeling](<https://devfeed.tech/tags/data-mining-modeling.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [google](<https://devfeed.tech/tags/google.md>), [icml](<https://devfeed.tech/tags/icml.md>), [icml-2026](<https://devfeed.tech/tags/icml-2026.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [research](<https://devfeed.tech/tags/research.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [rl](<https://devfeed.tech/tags/rl.md>), [search](<https://devfeed.tech/tags/search.md>)

### AI overview

Google Research presents Retrieve-for-Train, a framework that uses offline reinforcement learning to compile reward-aligned query fan-outs into training data for a lightweight diffusion retriever. The approach is intended to produce diverse, complementary, and coherent search-result sets in a single inference pass, reducing reliance on expensive inference-time reasoning.

### Source excerpt

Algorithms & Theory

## Build an AI-powered product tagging system with Amazon SageMaker serverless model customization

DevFeed: [Build an AI-powered product tagging system with Amazon SageMaker serverless model customization](<https://devfeed.tech/articles/build-an-ai-powered-product-tagging-system-with-amazon-sagemaker-serverless-model-customization-26940.md>)

Original publisher: [Read original article](<https://aws.amazon.com/blogs/machine-learning/build-an-ai-powered-product-tagging-system-with-amazon-sagemaker-serverless-model-customization/>)

Author: Linpo Guo

Published: 2026-09-15T16:11:36Z

Content type: tutorial

Language: en

Sources: [Artificial Intelligence](<https://devfeed.tech/sources/artificial-intelligence.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Amazon SageMaker AI](<https://devfeed.tech/topics/amazon-sagemaker-ai.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [rlvr](<https://devfeed.tech/topics/rlvr.md>), [grpo](<https://devfeed.tech/topics/grpo.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [SDK](<https://devfeed.tech/topics/sdk.md>), [Serverless](<https://devfeed.tech/topics/serverless.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [amazon-sagemaker](<https://devfeed.tech/tags/amazon-sagemaker.md>), [amazon-sagemaker-ai](<https://devfeed.tech/tags/amazon-sagemaker-ai.md>), [aws](<https://devfeed.tech/tags/aws.md>), [compute](<https://devfeed.tech/tags/compute.md>), [cost](<https://devfeed.tech/tags/cost.md>), [customization](<https://devfeed.tech/tags/customization.md>), [expert-400](<https://devfeed.tech/tags/expert-400.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [inference](<https://devfeed.tech/tags/inference.md>), [jobs](<https://devfeed.tech/tags/jobs.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [rlvr](<https://devfeed.tech/tags/rlvr.md>), [sdk](<https://devfeed.tech/tags/sdk.md>), [technical-how-to](<https://devfeed.tech/tags/technical-how-to.md>)

### AI overview

This walkthrough shows how to build a product tagging system by customizing Qwen3-8B with supervised fine-tuning and reinforcement learning with verifiable rewards on Amazon SageMaker serverless model customization. It then deploys the optimized model for asynchronous inference to enrich retail catalogs.

### Source excerpt

Manually tagging thousands of catalog products is slow and inconsistent. This walkthrough shows how to customize Qwen3-8B with supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) on Amazon SageMaker serverless model customization, then deploy it for asynchronous inference to build a cost-efficient product tagging system.

## Autonomous LLM post-training with Tunix on TPUs

DevFeed: [Autonomous LLM post-training with Tunix on TPUs](<https://devfeed.tech/articles/autonomous-llm-post-training-with-tunix-on-tpus-4205.md>)

Original publisher: [Read original article](<https://developers.googleblog.com/autonomous-llm-post-training-with-tunix-on-tpus/>)

Author: Wei Wei

Published: 2026-09-12T11:04:33.891311Z

Content type: article

Language: en

Sources: [Google Developers Blog](<https://devfeed.tech/sources/google-developers-blog.md>)

Topics: [post-training](<https://devfeed.tech/topics/post-training.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [grpo](<https://devfeed.tech/topics/grpo.md>), [lora](<https://devfeed.tech/topics/lora.md>), [gemma](<https://devfeed.tech/topics/gemma.md>), [Google](<https://devfeed.tech/topics/google.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [dataset](<https://devfeed.tech/topics/dataset.md>), [Command-line interface](<https://devfeed.tech/topics/cli.md>)

Tags: [autonomous](<https://devfeed.tech/tags/autonomous.md>), [cli](<https://devfeed.tech/tags/cli.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [gemma](<https://devfeed.tech/tags/gemma.md>), [git](<https://devfeed.tech/tags/git.md>), [google](<https://devfeed.tech/tags/google.md>), [grpo](<https://devfeed.tech/tags/grpo.md>), [llm](<https://devfeed.tech/tags/llm.md>), [lora](<https://devfeed.tech/tags/lora.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

This article presents autofinetune, an autonomous research loop for LLM post-training. Using AI agents and Google's AI stack, including Tunix, Gemma, Cloud TPUs, Antigravity CLI, and Gemini Flash 3.7, it automates supervised fine-tuning and reinforcement learning with GRPO, exploring hyperparameters such as LoRA configurations, learning rates, batch sizes, and rollout settings.

### Source excerpt

Imagine going to sleep after writing a single Markdown specification and waking up to find that an A...

## From MIT to IBM, expediting AI and quantum deployment

DevFeed: [From MIT to IBM, expediting AI and quantum deployment](<https://devfeed.tech/articles/from-mit-to-ibm-expediting-ai-and-quantum-deployment-37952.md>)

Original publisher: [Read original article](<https://news.mit.edu/2026/from-mit-to-ibm-expediting-ai-and-quantum-deployment-0902>)

Author: Lauren Hinkel | MIT-IBM Computing Research Lab

Published: 2026-09-02T20:25:00Z

Content type: news

Language: en

Sources: [MIT AI News](<https://devfeed.tech/sources/mit-ai-news.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [ibm](<https://devfeed.tech/topics/ibm.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [Computing](<https://devfeed.tech/topics/computing.md>), [LLMs](<https://devfeed.tech/topics/llms.md>)

Tags: [academic](<https://devfeed.tech/tags/academic.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [algorithms](<https://devfeed.tech/tags/algorithms.md>), [alumni-ae](<https://devfeed.tech/tags/alumni-ae.md>), [aram-harrow](<https://devfeed.tech/tags/aram-harrow.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [atari-games](<https://devfeed.tech/tags/atari-games.md>), [careers](<https://devfeed.tech/tags/careers.md>), [collaboration](<https://devfeed.tech/tags/collaboration.md>), [computer-science-and-artificial-intelligence-laboratory-csail](<https://devfeed.tech/tags/computer-science-and-artificial-intelligence-laboratory-csail.md>), [computer-science-and-technology](<https://devfeed.tech/tags/computer-science-and-technology.md>), [computing](<https://devfeed.tech/tags/computing.md>), [data](<https://devfeed.tech/tags/data.md>), [electrical-engineering-and-computer-science-eecs](<https://devfeed.tech/tags/electrical-engineering-and-computer-science-eecs.md>), [fair-ai](<https://devfeed.tech/tags/fair-ai.md>), [graduate-postdoctoral](<https://devfeed.tech/tags/graduate-postdoctoral.md>), [graduate-students](<https://devfeed.tech/tags/graduate-students.md>), [ibm](<https://devfeed.tech/tags/ibm.md>), [industry](<https://devfeed.tech/tags/industry.md>), [irene-ko](<https://devfeed.tech/tags/irene-ko.md>), [isaac-chuang](<https://devfeed.tech/tags/isaac-chuang.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [learning](<https://devfeed.tech/tags/learning.md>), [luca-daniel](<https://devfeed.tech/tags/luca-daniel.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [mit-ibm-computing-research-lab](<https://devfeed.tech/tags/mit-ibm-computing-research-lab.md>), [mit-ibm-watson-ai-lab](<https://devfeed.tech/tags/mit-ibm-watson-ai-lab.md>), [mit-schwarzman-college-of-computing](<https://devfeed.tech/tags/mit-schwarzman-college-of-computing.md>), [pulkit-agrawal](<https://devfeed.tech/tags/pulkit-agrawal.md>), [quantum-computing](<https://devfeed.tech/tags/quantum-computing.md>), [quantum-machine-learning](<https://devfeed.tech/tags/quantum-machine-learning.md>), [research](<https://devfeed.tech/tags/research.md>), [school-of-engineering](<https://devfeed.tech/tags/school-of-engineering.md>), [srinivasan-arunachalam](<https://devfeed.tech/tags/srinivasan-arunachalam.md>), [trustworthy-ai](<https://devfeed.tech/tags/trustworthy-ai.md>), [vllm-hook](<https://devfeed.tech/tags/vllm-hook.md>), [zhang-wei-hong](<https://devfeed.tech/tags/zhang-wei-hong.md>)

### AI overview

MIT graduate students and a former postdoc who moved to IBM describe how work with the MIT-IBM Computing Research Lab helped translate rigorous research into industry applications. Their areas include quantum machine learning, reinforcement learning, AI agents, and trustworthy and fair AI.

### Source excerpt

MIT affiliates engage with the MIT-IBM Computing Research Lab to bring rigorous theory to production systems.

## Perturbation Probing: A New Diagnostic for the Fragility of LLM Safety

DevFeed: [Perturbation Probing: A New Diagnostic for the Fragility of LLM Safety](<https://devfeed.tech/articles/perturbation-probing-a-new-diagnostic-for-the-fragility-of-llm-safety-7756.md>)

Original publisher: [Read original article](<https://unit42.paloaltonetworks.com/perturbation-probing-llm-safety/>)

Author: Tony Li, Hongliang Liu and Yuhao Wu

Published: 2026-08-28T22:00:07Z

Content type: article

Language: en

Sources: [Unit 42](<https://devfeed.tech/sources/unit-42.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [ai safety](<https://devfeed.tech/topics/ai-safety.md>), [Machine Learning, Security Attacks](<https://devfeed.tech/topics/machine-learning-security-attacks.md>), [Security](<https://devfeed.tech/topics/security.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [human feedback](<https://devfeed.tech/topics/human-feedback.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-safety](<https://devfeed.tech/tags/ai-safety.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [external](<https://devfeed.tech/tags/external.md>), [general](<https://devfeed.tech/tags/general.md>), [insights](<https://devfeed.tech/tags/insights.md>), [internals](<https://devfeed.tech/tags/internals.md>), [jailbreak](<https://devfeed.tech/tags/jailbreak.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llms](<https://devfeed.tech/tags/llms.md>), [model](<https://devfeed.tech/tags/model.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [research](<https://devfeed.tech/tags/research.md>), [safety](<https://devfeed.tech/tags/safety.md>), [security](<https://devfeed.tech/tags/security.md>)

### AI overview

The article presents perturbation probing, a low-cost method for identifying neurons causally responsible for targeted behaviors in aligned large language models. It reports that very small neuron subsets control refusal or false-agreement behaviors, suggesting that LLM safety can be fragile and concentrated rather than broadly distributed.

### Source excerpt

New research reveals that AI safety refusal lives in a thin neural layer, highlighting the critical need for external, multi-layered security. The post Perturbation Probing: A New Diagnostic for the Fragility of LLM Safety appeared first on Unit 42.

## How to Train a Cross-Embodiment Robot Navigation Policy with AI Agents

DevFeed: [How to Train a Cross-Embodiment Robot Navigation Policy with AI Agents](<https://devfeed.tech/articles/how-to-train-a-cross-embodiment-robot-navigation-policy-with-ai-agents-6861.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/how-to-train-a-cross-embodiment-robot-navigation-policy-with-ai-agents/>)

Author: Tanya Lenz

Published: 2026-08-26T20:05:06Z

Content type: tutorial

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [codex](<https://devfeed.tech/topics/codex.md>), [coding](<https://devfeed.tech/topics/coding.md>), [Simulation and Design](<https://devfeed.tech/topics/simulation-and-design.md>), [CUDA](<https://devfeed.tech/topics/cuda.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [codex](<https://devfeed.tech/tags/codex.md>), [coding](<https://devfeed.tech/tags/coding.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [development](<https://devfeed.tech/tags/development.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [framework](<https://devfeed.tech/tags/framework.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [omniverse](<https://devfeed.tech/tags/omniverse.md>), [physical-ai](<https://devfeed.tech/tags/physical-ai.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [robotics](<https://devfeed.tech/tags/robotics.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>), [skills](<https://devfeed.tech/tags/skills.md>), [testing](<https://devfeed.tech/tags/testing.md>), [training](<https://devfeed.tech/tags/training.md>), [tutorial](<https://devfeed.tech/tags/tutorial.md>), [workflow](<https://devfeed.tech/tags/workflow.md>)

### AI overview

This tutorial presents an agent-driven COMPASS workflow for training and evaluating cross-embodiment robot navigation policies. It covers asset preparation, smoke testing, residual reinforcement learning, checkpoint evaluation, runtime integration, and optional reconstructed environments using NVIDIA Omniverse NuRec.

### Source excerpt

Navigation enables a robot to turn perception and motion into purposeful autonomy. Unlike locomotion, which produces stable movement, navigation must be used to...

## Granite 4.2 brings native reasoning to enterprise agents

DevFeed: [Granite 4.2 brings native reasoning to enterprise agents](<https://devfeed.tech/articles/granite-4-2-brings-native-reasoning-to-enterprise-agents-17340.md>)

Original publisher: [Read original article](<https://research.ibm.com/blog/introducing-granite-4-2>)

Author: Mike Murphy; Kim Martineau

Published: 2026-08-25T15:00:00Z

Content type: release

Language: en

Sources: [IBM Research](<https://devfeed.tech/sources/ibm-research.md>)

Topics: [ibm](<https://devfeed.tech/topics/ibm.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Loop Engineering](<https://devfeed.tech/topics/loop-engineering.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [foundation-models](<https://devfeed.tech/topics/foundation-models.md>), [Software Engineering](<https://devfeed.tech/topics/software-engineering.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [apache](<https://devfeed.tech/tags/apache.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [ibm](<https://devfeed.tech/tags/ibm.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [release](<https://devfeed.tech/tags/release.md>), [terminal](<https://devfeed.tech/tags/terminal.md>)

### AI overview

IBM is releasing Granite 4.2 language models in 3B, 8B, and 30B sizes for enterprise agentic workflows. The models provide native reasoning, tool calling, instruction following, coding support, and deployment across cloud, on-premises, and edge environments. They are released under the Apache 2.0 license and trained with a multi-stage reinforcement learning process.

### Source excerpt

IBM's new open Granite models are designed for agentic AI, combining reasoning, tool use, coding, instruction following, and speech capabilities.

## From Atari to EVE Online: Building on 15 Years of AI Research in Games

DevFeed: [From Atari to EVE Online: Building on 15 Years of AI Research in Games](<https://devfeed.tech/articles/from-atari-to-eve-online-building-on-15-years-of-ai-research-in-games-6154.md>)

Original publisher: [Read original article](<https://deepmind.google/blog/from-atari-to-eve-online-building-on-15-years-of-ai-research-in-games/>)

Author: Alexandre Moufarek; Adrian Bolton

Published: 2026-08-21T11:59:48Z

Content type: article

Language: en

Sources: [Google DeepMind News](<https://devfeed.tech/sources/google-deepmind-news.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Game Development](<https://devfeed.tech/topics/game-development.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [Google](<https://devfeed.tech/topics/google.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [real-time](<https://devfeed.tech/topics/real-time.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [developers](<https://devfeed.tech/tags/developers.md>), [game-development](<https://devfeed.tech/tags/game-development.md>), [gaming](<https://devfeed.tech/tags/gaming.md>), [google](<https://devfeed.tech/tags/google.md>), [partnership](<https://devfeed.tech/tags/partnership.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [research](<https://devfeed.tech/tags/research.md>)

### AI overview

Google DeepMind describes 15 years of AI research driven by games, from Atari and Go to StarCraft II. The article highlights partnerships with game developers and milestones including DQN, AlphaGo, AlphaZero, MuZero, and AlphaStar, showing how game environments have advanced AI research and influenced gameplay strategies.

### Source excerpt

Google DeepMind partners with game studios to prototype breakthrough AI gameplay.

## Introducing CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement

DevFeed: [Introducing CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement](<https://devfeed.tech/articles/introducing-care-x-towards-clinically-useful-radiology-vlms-with-auxiliary-supervision-reward-aligned-learning-and-tool-augmented-measurement-6800.md>)

Original publisher: [Read original article](<https://www.microsoft.com/en-us/research/blog/introducing-care-x-towards-clinically-useful-radiology-vlms-with-auxiliary-supervision-reward-aligned-learning-and-tool-augmented-measurement/>)

Author: Mercy Ranjit, Nikhilesh E, Dr. Abhyuday Kumara Swamy, Tanuja Ganu

Published: 2026-08-11T16:00:00Z

Content type: article

Language: en

Sources: [Microsoft Research](<https://devfeed.tech/sources/microsoft-research.md>)

Topics: [vlm](<https://devfeed.tech/topics/vlm.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [data](<https://devfeed.tech/tags/data.md>), [generation](<https://devfeed.tech/tags/generation.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [research](<https://devfeed.tech/tags/research.md>), [research-blog](<https://devfeed.tech/tags/research-blog.md>), [tools](<https://devfeed.tech/tags/tools.md>), [vlms](<https://devfeed.tech/tags/vlms.md>)

### AI overview

CARE-X is a research chest X-ray vision-language model that combines free-text report generation, structured diagnostic prediction, and reinforcement learning for multi-task clinical interpretation. The article also describes a separate experiment using deterministic measurement tools with Qwen3-VL-4B-Instruct and reports validation on real-world Indian clinical data, while emphasizing that CARE-X is not approved for clinical use.

### Source excerpt

Radiology AI is evolving beyond report generation. CARE-X explores a unified approach that combines flexible reasoning, calibrated predictions, and measurement-based tools for chest X-ray interpretation. The post Introducing CARE-X: Towards Clinically Useful Radiology VLMs with Auxiliary Supervision, Reward-Aligned Learning, and Tool-Augmented Measurement appeared first on Microsoft Research.

## Generate Trajectories, Reasoning Traces, and Auto-Labels with NVIDIA Alpamayo 2 Super

DevFeed: [Generate Trajectories, Reasoning Traces, and Auto-Labels with NVIDIA Alpamayo 2 Super](<https://devfeed.tech/articles/generate-trajectories-reasoning-traces-and-auto-labels-with-nvidia-alpamayo-2-super-6828.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/generate-trajectories-reasoning-traces-and-auto-labels-with-nvidia-alpamayo-2-super/>)

Author: Elizabeth Goodman

Published: 2026-08-04T15:00:00Z

Content type: tutorial

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [foundation-models](<https://devfeed.tech/topics/foundation-models.md>), [Cosmos](<https://devfeed.tech/topics/cosmos.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [GitHub](<https://devfeed.tech/topics/github.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Open Source Models & Datasets](<https://devfeed.tech/topics/open-source-models-datasets.md>)

Tags: [accelerate](<https://devfeed.tech/tags/accelerate.md>), [automotive-transportation](<https://devfeed.tech/tags/automotive-transportation.md>), [autonomous](<https://devfeed.tech/tags/autonomous.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [cosmos](<https://devfeed.tech/tags/cosmos.md>), [customization](<https://devfeed.tech/tags/customization.md>), [data](<https://devfeed.tech/tags/data.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [developers](<https://devfeed.tech/tags/developers.md>), [development](<https://devfeed.tech/tags/development.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [drive](<https://devfeed.tech/tags/drive.md>), [driving](<https://devfeed.tech/tags/driving.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [featured](<https://devfeed.tech/tags/featured.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [generate](<https://devfeed.tech/tags/generate.md>), [generation](<https://devfeed.tech/tags/generation.md>), [github](<https://devfeed.tech/tags/github.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [inference](<https://devfeed.tech/tags/inference.md>), [learning](<https://devfeed.tech/tags/learning.md>), [models](<https://devfeed.tech/tags/models.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [robot-navigation](<https://devfeed.tech/tags/robot-navigation.md>), [robotics](<https://devfeed.tech/tags/robotics.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>)

### AI overview

NVIDIA Alpamayo 2 Super is an open 34-billion-parameter reasoning vision-language-action model for autonomous vehicle development. It combines NVIDIA Cosmos 3 Super Reasoner with a diffusion-based Action Expert to generate trajectories, reasoning traces, meta-actions, scene answers, and auto-labels across development workflows.

### Source excerpt

Autonomous vehicle (AV) development often relies on separate models for trajectory generation, high-level intent prediction, scene understanding, and data...

## Чем запомнилась ICRA 2026: Reinforcement Learning, генерация сложных сценариев поведения и будущее робототехники

DevFeed: [Чем запомнилась ICRA 2026: Reinforcement Learning, генерация сложных сценариев поведения и будущее робототехники](<https://devfeed.tech/articles/icra-2026-reinforcement-learning-24875.md>)

Original publisher: [Read original article](<https://habr.com/ru/companies/yandex/articles/1065938/>)

Author: egavolk (Яндекс)

Published: 2026-08-04T08:00:45Z

Content type: article

Language: ru

Sources: [Яндекс - Как мы делаем Яндекс / Статьи](<https://devfeed.tech/sources/source.md>)

Topics: [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [Robotics](<https://devfeed.tech/topics/robotics.md>), [Simulation](<https://devfeed.tech/topics/simulation.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>)

Tags: [2026](<https://devfeed.tech/tags/2026.md>), [icra](<https://devfeed.tech/tags/icra.md>), [ml](<https://devfeed.tech/tags/ml.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [rl](<https://devfeed.tech/tags/rl.md>), [robotics](<https://devfeed.tech/tags/robotics.md>), [simulation](<https://devfeed.tech/tags/simulation.md>), [tag-511fbf58fd45](<https://devfeed.tech/tags/tag-511fbf58fd45.md>), [tag-6faff4be08e9](<https://devfeed.tech/tags/tag-6faff4be08e9.md>), [tag-d704a344cc75](<https://devfeed.tech/tags/tag-d704a344cc75.md>), [tag-dace475544fb](<https://devfeed.tech/tags/tag-dace475544fb.md>)

### AI overview

The article reviews notable trends, papers, and engineering trade-offs discussed at ICRA 2026, with emphasis on reinforcement learning, autonomous-vehicle perception and planning pipelines, simulation, rare edge-case generation, and robotic learning. It also discusses award-winning work on manipulation, humanoid robots, and camera-conditioned policy learning.

### Source excerpt

Привет, Хабр! В начале июня в Вене прошла главная международная конференция по робототехнике и автономным системам -- International Conference on Robotics and Automation (ICRA). В этом году среди участников была и наша команда автономного транспорта Яндекса. Топиков, которые обсуждаются на ICRA, много, потому что она не только об ML -- она скорее о робототехнике в целом. Например, есть секции о механизмах и дизайне, а также о медицинских роботах. Было немало и чисто инженерных работ. Ключевой топик докладов на конференции -- RL, он же Reinforcement Learning, обучение с подкреплением. Также нас интересовали статьи по классическому пайплайну автономного автомобиля: perception + prediction + planner + simulation. Новые подходы к Robotic Learning тоже интересны, так как их можно перенести на задачи автономного транспорта. Меня зовут Егор Волков, я занимаюсь претрейном модели планирования движения в автономном транспорте Яндекса. Вместе со мной на конференцию ездил Максим Спорышев -- руководитель службы поведения и предсказания движения. В этой статье мы собрали самые интересные тренды, доклады и инженерные развилки, которые заметили на ICRA 2026, -- от Reinforcement Learning и генерации редких edge-кейсов до того, куда вообще двигается ML в робототехнике. Читать далее

## Orchard: An open framework for scalable agentic AI

DevFeed: [Orchard: An open framework for scalable agentic AI](<https://devfeed.tech/articles/orchard-an-open-framework-for-scalable-agentic-ai-6806.md>)

Original publisher: [Read original article](<https://www.microsoft.com/en-us/research/blog/orchard-an-open-framework-for-scalable-agentic-ai/>)

Author: Baolin Peng, Wenlin Yao, Qianhui Wu, Hao Cheng, Jianfeng Gao

Published: 2026-08-03T16:00:00Z

Content type: article

Language: en

Sources: [Microsoft Research](<https://devfeed.tech/sources/microsoft-research.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [AI Research](<https://devfeed.tech/topics/ai-research.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Frameworks](<https://devfeed.tech/topics/frameworks.md>), [Kubernetes](<https://devfeed.tech/topics/kubernetes.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [computer-use](<https://devfeed.tech/topics/computer-use.md>), [codex](<https://devfeed.tech/topics/codex.md>), [OpenClaw](<https://devfeed.tech/topics/openclaw.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [autonomous](<https://devfeed.tech/tags/autonomous.md>), [autonomous-agents](<https://devfeed.tech/tags/autonomous-agents.md>), [building](<https://devfeed.tech/tags/building.md>), [codex](<https://devfeed.tech/tags/codex.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [framework](<https://devfeed.tech/tags/framework.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [learning](<https://devfeed.tech/tags/learning.md>), [models](<https://devfeed.tech/tags/models.md>), [open](<https://devfeed.tech/tags/open.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [research](<https://devfeed.tech/tags/research.md>), [research-blog](<https://devfeed.tech/tags/research-blog.md>), [train](<https://devfeed.tech/tags/train.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

### AI overview

Orchard is an open-source framework for training and evaluating agentic AI systems across software engineering, web navigation, and personal-assistant tasks. Its reusable Orchard Env provides Kubernetes-based infrastructure for data collection, reinforcement-learning rollouts, and evaluation, while Orchard-SWE, Orchard-GUI, and Orchard-Claw demonstrate strong results from relatively small open-weight models.

### Source excerpt

Orchard is an open-source framework for the research community to train and evaluate AI agents across task types. It reduces complexity while supporting strong performance from smaller models by enabling researchers to reuse the same infrastructure. The post Orchard: An open framework for scalable agentic AI appeared first on Microsoft Research.

## Echoverse: Deep, evolving environments for computer-use agents

DevFeed: [Echoverse: Deep, evolving environments for computer-use agents](<https://devfeed.tech/articles/echoverse-deep-evolving-environments-for-computer-use-agents-6787.md>)

Original publisher: [Read original article](<https://www.microsoft.com/en-us/research/blog/echoverse-deep-evolving-environments-for-computer-use-agents/>)

Author: Akshay Nambi, Yash Pandya, Sahil Gupta, Sarthak Harne, Kavyansh Chourasia, Yash Lara, Ahmed Awadallah

Published: 2026-07-30T17:00:00Z

Content type: article

Language: en

Sources: [Microsoft Research](<https://devfeed.tech/sources/microsoft-research.md>)

Topics: [computer-use](<https://devfeed.tech/topics/computer-use.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Microsoft](<https://devfeed.tech/topics/microsoft.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [computer-use](<https://devfeed.tech/tags/computer-use.md>), [data](<https://devfeed.tech/tags/data.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [microsoft](<https://devfeed.tech/tags/microsoft.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [research](<https://devfeed.tech/tags/research.md>), [research-blog](<https://devfeed.tech/tags/research-blog.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

Echoverse presents twelve high-fidelity training worlds for computer-use agents, designed around realistic application behavior, coherent state, seeded data, and challenging interface controls. Training a 9B model on these environments substantially improved its score, while reinforcement learning with grounded verification helped it generalize and complete goals in fewer steps. Four worlds are released with code, data, and graders to support research.

### Source excerpt

Computer-use AI agents struggle with multi-step workflows like email and customer support. Echoverse trains agents in realistic environments rather than simply providing more training tasks, helping them improve as the tasks, tests, and environments evolve. The post Echoverse: Deep, evolving environments for computer-use agents appeared first on Microsoft Research.

## Developing Healthcare Robotics with GPU-Native Medical Physics Simulation

DevFeed: [Developing Healthcare Robotics with GPU-Native Medical Physics Simulation](<https://devfeed.tech/articles/developing-healthcare-robotics-with-gpu-native-medical-physics-simulation-6808.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/developing-healthcare-robotics-with-gpu-native-medical-physics-simulation/>)

Author: Michelle Horton

Published: 2026-07-28T20:49:21Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Isaac for Healthcare](<https://devfeed.tech/topics/isaac-for-healthcare.md>), [Robotics](<https://devfeed.tech/topics/robotics.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Medical imaging](<https://devfeed.tech/topics/medical-imaging.md>), [Cosmos](<https://devfeed.tech/topics/cosmos.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [real-time](<https://devfeed.tech/topics/real-time.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [data](<https://devfeed.tech/tags/data.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [healthcare](<https://devfeed.tech/tags/healthcare.md>), [isaac](<https://devfeed.tech/tags/isaac.md>), [isaac-for-healthcare](<https://devfeed.tech/tags/isaac-for-healthcare.md>), [isaac-sim](<https://devfeed.tech/tags/isaac-sim.md>), [medical-imaging](<https://devfeed.tech/tags/medical-imaging.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [physical-ai](<https://devfeed.tech/tags/physical-ai.md>), [physics](<https://devfeed.tech/tags/physics.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [robotics](<https://devfeed.tech/tags/robotics.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>), [warp](<https://devfeed.tech/tags/warp.md>), [world-model](<https://devfeed.tech/tags/world-model.md>)

### AI overview

The article presents NVIDIA's open source, GPU-accelerated Medical Physics Simulation framework for healthcare robotics. It addresses limited medical robotics data, poor generalization, and slow development by enabling anatomical digital twins, device-anatomy and medical imaging simulation, and GPU-scale reinforcement learning within Isaac for Healthcare, Isaac Sim, and Isaac Lab.

### Source excerpt

Unlike autonomous driving or industrial robotics, healthcare robotics can't rely on internet-scale data collection or unlimited real-world experimentation....

## Start Customizing NVIDIA Nemotron 3 Nano with Prime Intellect Lab in Minutes

DevFeed: [Start Customizing NVIDIA Nemotron 3 Nano with Prime Intellect Lab in Minutes](<https://devfeed.tech/articles/start-customizing-nvidia-nemotron-3-nano-with-prime-intellect-lab-in-minutes-6942.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/start-customizing-nvidia-nemotron-3-nano-with-prime-intellect-lab-in-minutes/>)

Author: Chris Alexiuk

Published: 2026-07-23T16:00:00Z

Content type: tutorial

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [rlvr](<https://devfeed.tech/topics/rlvr.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [Python](<https://devfeed.tech/topics/python.md>), [coding](<https://devfeed.tech/topics/coding.md>), [math](<https://devfeed.tech/topics/math.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [blog](<https://devfeed.tech/tags/blog.md>), [coding](<https://devfeed.tech/tags/coding.md>), [customization](<https://devfeed.tech/tags/customization.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [developers](<https://devfeed.tech/tags/developers.md>), [experiment](<https://devfeed.tech/tags/experiment.md>), [featured](<https://devfeed.tech/tags/featured.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [getting-started](<https://devfeed.tech/tags/getting-started.md>), [math](<https://devfeed.tech/tags/math.md>), [nemotron](<https://devfeed.tech/tags/nemotron.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open](<https://devfeed.tech/tags/open.md>), [python](<https://devfeed.tech/tags/python.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [rlvr](<https://devfeed.tech/tags/rlvr.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>), [train](<https://devfeed.tech/tags/train.md>), [training](<https://devfeed.tech/tags/training.md>), [tutorial](<https://devfeed.tech/tags/tutorial.md>)

### AI overview

This tutorial shows how to customize NVIDIA Nemotron 3 Nano with Prime Intellect Lab using reinforcement learning with verifiable rewards on a Python Math environment. It covers a baseline-training-reevaluation workflow and produces a downloadable LoRA adapter.

### Source excerpt

Customization is what enables developers to take a general model and tailor it to use cases, domains, languages, and more. However, customization comes with a...

## Towards a quantum computer that learns from its errors

DevFeed: [Towards a quantum computer that learns from its errors](<https://devfeed.tech/articles/towards-a-quantum-computer-that-learns-from-its-errors-6906.md>)

Original publisher: [Read original article](<https://research.google/blog/towards-a-quantum-computer-that-learns-from-its-errors/>)

Published: 2026-07-22T18:40:21Z

Content type: article

Language: en

Sources: [The latest research from Google](<https://devfeed.tech/sources/the-latest-research-from-google.md>)

Topics: [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [autonomous](<https://devfeed.tech/tags/autonomous.md>), [errors](<https://devfeed.tech/tags/errors.md>), [learning](<https://devfeed.tech/tags/learning.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [quantum](<https://devfeed.tech/tags/quantum.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [research](<https://devfeed.tech/tags/research.md>), [rl](<https://devfeed.tech/tags/rl.md>)

### AI overview

Google Research describes a reinforcement-learning framework that uses quantum error detections to continuously adjust control parameters during computation, helping stabilize a quantum computer against drift. The article also discusses quantum error correction and the AlphaQubit neural-network decoder.

### Source excerpt

Machine Intelligence

## Professor Emeritus Dimitri Bertsekas, influential computer scientist and prolific author, dies at 83

DevFeed: [Professor Emeritus Dimitri Bertsekas, influential computer scientist and prolific author, dies at 83](<https://devfeed.tech/articles/professor-emeritus-dimitri-bertsekas-influential-computer-scientist-and-prolific-author-dies-at-83-37950.md>)

Original publisher: [Read original article](<https://news.mit.edu/2026/dimitri-bertsekas-influential-computer-scientist-prolific-author-dies-0722>)

Author: Jane Halpern | Department of Electrical Engineering and Computer Science

Published: 2026-07-22T17:00:00Z

Content type: news

Language: en

Sources: [MIT AI News](<https://devfeed.tech/sources/mit-ai-news.md>)

Topics: [Optimization](<https://devfeed.tech/topics/optimization.md>), [Computer science](<https://devfeed.tech/topics/computer-science.md>), [Electrical engineering and computer science (EECS)](<https://devfeed.tech/topics/electrical-engineering-and-computer-science-eecs.md>), [AI Research](<https://devfeed.tech/topics/ai-research.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>)

Tags: [alumni-ae](<https://devfeed.tech/tags/alumni-ae.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [asu-ozdaglar](<https://devfeed.tech/tags/asu-ozdaglar.md>), [athena-scientific](<https://devfeed.tech/tags/athena-scientific.md>), [bayforest-technologies](<https://devfeed.tech/tags/bayforest-technologies.md>), [books-and-authors](<https://devfeed.tech/tags/books-and-authors.md>), [computer-science](<https://devfeed.tech/tags/computer-science.md>), [computer-science-and-technology](<https://devfeed.tech/tags/computer-science-and-technology.md>), [convex-analysis](<https://devfeed.tech/tags/convex-analysis.md>), [dimitri-bertsekas](<https://devfeed.tech/tags/dimitri-bertsekas.md>), [electrical-engineering-and-computer-science-eecs](<https://devfeed.tech/tags/electrical-engineering-and-computer-science-eecs.md>), [faculty](<https://devfeed.tech/tags/faculty.md>), [laboratory-for-information-and-decision-systems-lids](<https://devfeed.tech/tags/laboratory-for-information-and-decision-systems-lids.md>), [large-scale-computation](<https://devfeed.tech/tags/large-scale-computation.md>), [mit-books-and-authors](<https://devfeed.tech/tags/mit-books-and-authors.md>), [mit-eecs-faculty](<https://devfeed.tech/tags/mit-eecs-faculty.md>), [mit-faculty-obituary](<https://devfeed.tech/tags/mit-faculty-obituary.md>), [mit-lids](<https://devfeed.tech/tags/mit-lids.md>), [mit-photographers](<https://devfeed.tech/tags/mit-photographers.md>), [mit-sandbox-innovation-fund-program](<https://devfeed.tech/tags/mit-sandbox-innovation-fund-program.md>), [mit-schwarzman-college-of-computing](<https://devfeed.tech/tags/mit-schwarzman-college-of-computing.md>), [munther-dahleh](<https://devfeed.tech/tags/munther-dahleh.md>), [network-optimization](<https://devfeed.tech/tags/network-optimization.md>), [networks](<https://devfeed.tech/tags/networks.md>), [neurodynamic-programming](<https://devfeed.tech/tags/neurodynamic-programming.md>), [obituaries](<https://devfeed.tech/tags/obituaries.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [photography](<https://devfeed.tech/tags/photography.md>), [programming](<https://devfeed.tech/tags/programming.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [research](<https://devfeed.tech/tags/research.md>), [robert-gallager](<https://devfeed.tech/tags/robert-gallager.md>), [school-of-engineering](<https://devfeed.tech/tags/school-of-engineering.md>)

### AI overview

An obituary remembers Dimitri Bertsekas, an MIT professor emeritus and influential computer scientist who died at 83. His work shaped optimization, control, large-scale computation, reinforcement learning, and artificial intelligence, while his books and mentorship influenced generations of students and researchers.

### Source excerpt

Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence.

## Capturing token IDs during agentic interactions for better reinforcement learning

DevFeed: [Capturing token IDs during agentic interactions for better reinforcement learning](<https://devfeed.tech/articles/capturing-token-ids-during-agentic-interactions-for-better-reinforcement-learning-7595.md>)

Original publisher: [Read original article](<https://www.amazon.science/blog/capturing-token-ids-during-agentic-interactions-for-better-reinforcement-learning>)

Author: Frederick Robinson

Published: 2026-07-09T12:46:00Z

Content type: article

Language: en

Sources: [Amazon Science homepage](<https://devfeed.tech/sources/amazon-science-homepage.md>)

Topics: [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [Agent Harness](<https://devfeed.tech/topics/agent-harness.md>), [tokenization](<https://devfeed.tech/topics/tokenization.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [Rust](<https://devfeed.tech/topics/rust.md>), [computer-use](<https://devfeed.tech/topics/computer-use.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [amazon-agi-lab](<https://devfeed.tech/tags/amazon-agi-lab.md>), [computer-use](<https://devfeed.tech/tags/computer-use.md>), [dataset-development](<https://devfeed.tech/tags/dataset-development.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [model](<https://devfeed.tech/tags/model.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [rust](<https://devfeed.tech/tags/rust.md>), [tokens](<https://devfeed.tech/tags/tokens.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

This article explains how Turnstile, a Rust proxy placed between an agent harness and a model backend, captures exact token IDs during generation. The recorded token-level trajectories preserve information that text transcripts can lose and can be passed into reinforcement-learning training stacks. Reported validations cover a text-only coding agent and a multimodal computer-use agent whose performance improved during RL runs.

### Source excerpt

A new Rust proxy called Turnstile sits between the model backend and the agent harness to capture information lost in mere text transcripts.

## Introducing Robostral Navigate

DevFeed: [Introducing Robostral Navigate](<https://devfeed.tech/articles/introducing-robostral-navigate-7115.md>)

Original publisher: [Read original article](<https://mistral.ai/news/robostral-navigate/>)

Published: 2026-07-08T12:00:59Z

Content type: news

Language: en

Sources: [Mistral AI Blog](<https://devfeed.tech/sources/mistral-ai-blog.md>)

Topics: [Robotics](<https://devfeed.tech/topics/robotics.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [Simulation and Design](<https://devfeed.tech/topics/simulation-and-design.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [model](<https://devfeed.tech/tags/model.md>), [models](<https://devfeed.tech/tags/models.md>), [performance](<https://devfeed.tech/tags/performance.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [robotics](<https://devfeed.tech/tags/robotics.md>), [robots](<https://devfeed.tech/tags/robots.md>), [technology](<https://devfeed.tech/tags/technology.md>), [training](<https://devfeed.tech/tags/training.md>), [validation](<https://devfeed.tech/tags/validation.md>)

### AI overview

Robostral Navigate is an 8B embodied-navigation model that guides robots through complex environments from a single RGB camera and plain-language instructions. It achieves 76.6% success on unseen R2R-CE validation, outperforming systems using depth or multiple cameras, and runs across wheeled, legged, and flying robots.

### Source excerpt

Introducing Robostral Navigate: 8B model achieving 76.6% on R2R-CE with just a single RGB camera. No depth sensors, LiDAR, or multiple cameras needed.

## Capital One at ACL 2026

DevFeed: [Capital One at ACL 2026](<https://devfeed.tech/articles/capital-one-at-acl-2026-22571.md>)

Original publisher: [Read original article](<https://medium.com/capital-one-tech/capital-one-at-acl-2026-ad9c245333fe?source=rss----3db3a67cb648---4>)

Author: Capital One Tech

Published: 2026-07-01T15:28:51Z

Content type: article

Language: en

Sources: [Capital One Tech](<https://devfeed.tech/sources/capital-one-tech.md>)

Topics: [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [LLM security](<https://devfeed.tech/topics/llm-security.md>), [Machine Learning, Security Attacks](<https://devfeed.tech/topics/machine-learning-security-attacks.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [Jailbreak](<https://devfeed.tech/topics/jailbreak.md>), [Security](<https://devfeed.tech/topics/security.md>), [Routing (disambiguation)](<https://devfeed.tech/topics/routing.md>), [datasets](<https://devfeed.tech/topics/datasets.md>)

Tags: [2026](<https://devfeed.tech/tags/2026.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-ml](<https://devfeed.tech/tags/ai-ml.md>), [ai-research](<https://devfeed.tech/tags/ai-research.md>), [conference](<https://devfeed.tech/tags/conference.md>), [jailbreak](<https://devfeed.tech/tags/jailbreak.md>), [llm-security](<https://devfeed.tech/tags/llm-security.md>), [natural-language-processing](<https://devfeed.tech/tags/natural-language-processing.md>), [paper](<https://devfeed.tech/tags/paper.md>), [partners](<https://devfeed.tech/tags/partners.md>), [red-teaming](<https://devfeed.tech/tags/red-teaming.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [research](<https://devfeed.tech/tags/research.md>), [science](<https://devfeed.tech/tags/science.md>)

### AI overview

Capital One describes its accepted ACL 2026 research on natural language processing, including work on adaptive LLM red teaming, query-only model routing with generated data, and language identification on web data. The article also highlights collaboration with academic partners.

### Source excerpt

Discover how Capital One is advancing state-of-the-art AI/ML science through collaborative natural language processing research.Advancing AI and NLP Frontiers at ACL 2026 As language models grow more deeply integrated into technology ecosystems, pioneering robust, efficient, and reliable Natural Language Processing (NLP) techniques becomes paramount. Capital One continues to invest in state-of-the-art AI/ML science through deep multi-sector collaboration and peer-reviewed research. At the upcoming Annual Meeting of the Association for Computational Linguistics (ACL 2026), Capital One researchers and academic partners will showcase novel findings stretching from LLM security to multilingual capabilities. Through the Science & Academic Partnerships program, Capital One bridges industry needs with academic expertise, funding critical university research and engineering solutions that make technology safer and more powerful. Our accepted publications at ACL 2026 demonstrate this thriving flywheel of talent and collaborative innovation across multiple research categories. Main Conference Research Adaptive Instruction Composition for Automated LLM Red Teaming Routing with Generated Data: Annotation-Free LLM Skill Estimation and Expert Selection Capital One Authors: Jesse Zymet, Swapnil Shinde, Sahil Wadhwa, Andy Luo Overview: Standard red teaming approaches often struggle with a limited range of jailbreak strategies or rely on ineffective, randomized crowd-sourced tactics. This paper introduces a novel framework -- Adaptive Instruction Composition -- that utilizes reinforcement learning and a neural contextual bandit to tailor attack compositions dynamically, balancing diversity and effectiveness to proactively uncover target model vulnerabilities. Routing with Generated Data: Annotation-Free LLM Skill Estimation and Expert Selection Capital One Authors: Genta Winata, Sambit Sahu, Supriyo Chakraborty, Shixiong Zhang Overview: Emerging from our gifted research collaboration

## Optimizing a Neural Reconstruction Pipeline Using NVIDIA Nsight Developer Tools

DevFeed: [Optimizing a Neural Reconstruction Pipeline Using NVIDIA Nsight Developer Tools](<https://devfeed.tech/articles/optimizing-a-neural-reconstruction-pipeline-using-nvidia-nsight-developer-tools-6918.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/optimizing-a-neural-reconstruction-pipeline-using-nvidia-nsight-developer-tools/>)

Author: Tanya Lenz

Published: 2026-06-30T16:00:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Omniverse](<https://devfeed.tech/topics/omniverse.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Tooling](<https://devfeed.tech/topics/tooling.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [CUDA](<https://devfeed.tech/topics/cuda.md>), [PyTorch](<https://devfeed.tech/topics/pytorch.md>), [Physical AI](<https://devfeed.tech/topics/physical-ai.md>), [Robotics](<https://devfeed.tech/topics/robotics.md>), [Synthetic Data Generation](<https://devfeed.tech/topics/synthetic-data-generation.md>), [real-time](<https://devfeed.tech/topics/real-time.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>)

Tags: [3d](<https://devfeed.tech/tags/3d.md>), [ai](<https://devfeed.tech/tags/ai.md>), [autonomous](<https://devfeed.tech/tags/autonomous.md>), [autonomous-vehicles](<https://devfeed.tech/tags/autonomous-vehicles.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [developer-tools](<https://devfeed.tech/tags/developer-tools.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [driving](<https://devfeed.tech/tags/driving.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [lidar](<https://devfeed.tech/tags/lidar.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [omniverse](<https://devfeed.tech/tags/omniverse.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [performance](<https://devfeed.tech/tags/performance.md>), [physical-ai](<https://devfeed.tech/tags/physical-ai.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>), [synthetic-data-generation](<https://devfeed.tech/tags/synthetic-data-generation.md>)

### AI overview

This article explains how NVIDIA Nsight Developer Tools can optimize the NVIDIA Omniverse NuRec neural reconstruction pipeline. It focuses on reducing GPU-intensive reconstruction and rendering costs to improve engineering iteration and move toward real-time performance.

### Source excerpt

NVIDIA Omniverse NuRec is a neural reconstruction pipeline for building high-fidelity 3D representations of real-world environments from multisensor data such...

## Datadog acquires Adaptive ML

DevFeed: [Datadog acquires Adaptive ML](<https://devfeed.tech/articles/datadog-acquires-adaptive-ml-2255.md>)

Original publisher: [Read original article](<https://www.datadoghq.com/blog/datadog-acquires-adaptive-ml/>)

Author: Alexis Lê-Quôc

Published: 2026-06-30T00:00:00Z

Content type: news

Language: en

Sources: [Datadog | The Monitor blog](<https://devfeed.tech/sources/datadog-the-monitor-blog.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [agent observability](<https://devfeed.tech/topics/agent-observability.md>), [Frontier AI](<https://devfeed.tech/topics/frontier-ai.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [Synthetic Data Generation](<https://devfeed.tech/topics/synthetic-data-generation.md>), [A/B Testing](<https://devfeed.tech/topics/a-b-testing.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>), [observability](<https://devfeed.tech/topics/observability.md>), [Security](<https://devfeed.tech/topics/security.md>), [real-time](<https://devfeed.tech/topics/real-time.md>)

Tags: [a-b-testing](<https://devfeed.tech/tags/a-b-testing.md>), [acquisition](<https://devfeed.tech/tags/acquisition.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [autonomous](<https://devfeed.tech/tags/autonomous.md>), [data](<https://devfeed.tech/tags/data.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [frontier-ai](<https://devfeed.tech/tags/frontier-ai.md>), [models](<https://devfeed.tech/tags/models.md>), [observability](<https://devfeed.tech/tags/observability.md>), [platform](<https://devfeed.tech/tags/platform.md>), [production](<https://devfeed.tech/tags/production.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [security](<https://devfeed.tech/tags/security.md>), [synthetic-data-generation](<https://devfeed.tech/tags/synthetic-data-generation.md>)

### AI overview

Datadog announces the acquisition of Adaptive ML, whose Adaptive Engine helps enterprises build, own, and deploy specialized AI agents and models. The platform supports fine-tuning open models with reinforcement learning and synthetic data, evaluating them with AI judges and A/B testing, and using production signals to improve subsequent training.

### Source excerpt

Datadog has acquired Adaptive ML, a platform for building, owning, and deploying specialized AI agents and models.

[Next page](<https://devfeed.tech/topics/reinforcement-learning.md?cursor=WyIyMDI2LTA2LTMwVDAwOjAwOjAwKzAwOjAwIiwgImZiYzA0NmZkLWVhYzItNDc5Ny1hMGZlLTlhNzBlNjFjZmVkYyJd>)