# Reinforcement Learning

Published articles for Reinforcement Learning.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## 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...

## OpenAI's Greg Brockman discusses computer use as an alternative to purpose-built AI agent integrations

DevFeed: [OpenAI's Greg Brockman discusses computer use as an alternative to purpose-built AI agent integrations](<https://devfeed.tech/articles/openai-president-the-computer-should-be-there-to-empower-you-so-stop-retooling-software-for-ai-agents-26950.md>)

Original publisher: [Read original article](<https://thenewstack.io/computer-use-agent-connectors/>)

Author: Meredith Shubel

Published: 2026-09-15T23:41:22Z

Content type: opinion

Language: en

Sources: [The New Stack](<https://devfeed.tech/sources/the-new-stack.md>)

Topics: [computer-use](<https://devfeed.tech/topics/computer-use.md>), [OpenAI](<https://devfeed.tech/topics/openai.md>), [Model Context Protocol](<https://devfeed.tech/topics/model-context-protocol.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>)

Tags: [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [computer-use](<https://devfeed.tech/tags/computer-use.md>), [developer-tools](<https://devfeed.tech/tags/developer-tools.md>), [developers](<https://devfeed.tech/tags/developers.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [model-context-protocol](<https://devfeed.tech/tags/model-context-protocol.md>), [openai](<https://devfeed.tech/tags/openai.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>)

### AI overview

OpenAI president and co-founder Greg Brockman discusses whether AI agents could use computers through screens, keyboards, and mice instead of relying on purpose-built MCP servers, CLIs, APIs, and other integrations.

### Source excerpt

This week on the a16z show, Greg Brockman, president and co-founder of OpenAI, made the point that developers have been The post OpenAI president: "The computer should be there to empower you." So stop retooling software for AI agents appeared first on The New Stack.

## 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...

## OpenAI's safety system is already cutting off API responses mid-task

DevFeed: [OpenAI's safety system is already cutting off API responses mid-task](<https://devfeed.tech/articles/openai-s-safety-system-is-already-cutting-off-api-responses-mid-task-8485.md>)

Original publisher: [Read original article](<https://thenewstack.io/openai-slowing-ai-development/>)

Author: Amanda Caswell

Published: 2026-09-11T17:52:56Z

Content type: news

Language: en

Sources: [The New Stack](<https://devfeed.tech/sources/the-new-stack.md>)

Topics: [releases](<https://devfeed.tech/topics/releases.md>), [AI Bots](<https://devfeed.tech/topics/ai-bots.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-models](<https://devfeed.tech/tags/ai-models.md>), [ai-strategy](<https://devfeed.tech/tags/ai-strategy.md>), [api](<https://devfeed.tech/tags/api.md>), [cybersecurity](<https://devfeed.tech/tags/cybersecurity.md>), [developers](<https://devfeed.tech/tags/developers.md>), [model-development](<https://devfeed.tech/tags/model-development.md>), [models](<https://devfeed.tech/tags/models.md>), [openai](<https://devfeed.tech/tags/openai.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [release](<https://devfeed.tech/tags/release.md>), [responses](<https://devfeed.tech/tags/responses.md>), [safety](<https://devfeed.tech/tags/safety.md>), [software-development](<https://devfeed.tech/tags/software-development.md>)

### AI overview

OpenAI is reportedly considering slower development of its most advanced AI systems as safety concerns could delay releases and limit access. The article cites pauses in model work and restrictions following cybersecurity evaluations and an AI-agent containment incident.

### Source excerpt

AI companies have spent the last few years competing to build the best models, faster than the other, with each The post OpenAI's safety system is already cutting off API responses mid-task appeared first on The New Stack.

## GPT-6 Astra: A new generation of intelligence

DevFeed: [GPT-6 Astra: A new generation of intelligence](<https://devfeed.tech/articles/gpt-6-astra-a-new-generation-of-intelligence-6439.md>)

Original publisher: [Read original article](<https://openai.com/index/gpt-6-astra>)

Published: 2026-09-03T11:00:00Z

Content type: release

Language: en

Sources: [OpenAI News](<https://devfeed.tech/sources/openai-news.md>)

Topics: [computer-use](<https://devfeed.tech/topics/computer-use.md>), [coding](<https://devfeed.tech/topics/coding.md>), [Software Engineering](<https://devfeed.tech/topics/software-engineering.md>), [Cybersecurity](<https://devfeed.tech/topics/cybersecurity.md>), [Amazon Bedrock](<https://devfeed.tech/topics/amazon-bedrock.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>)

Tags: [api](<https://devfeed.tech/tags/api.md>), [aws](<https://devfeed.tech/tags/aws.md>), [azure](<https://devfeed.tech/tags/azure.md>), [bedrock](<https://devfeed.tech/tags/bedrock.md>), [browser](<https://devfeed.tech/tags/browser.md>), [chatgpt](<https://devfeed.tech/tags/chatgpt.md>), [coding](<https://devfeed.tech/tags/coding.md>), [computer-use](<https://devfeed.tech/tags/computer-use.md>), [cybersecurity](<https://devfeed.tech/tags/cybersecurity.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [gpt](<https://devfeed.tech/tags/gpt.md>), [latency](<https://devfeed.tech/tags/latency.md>), [model](<https://devfeed.tech/tags/model.md>), [openai](<https://devfeed.tech/tags/openai.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [research](<https://devfeed.tech/tags/research.md>), [safety](<https://devfeed.tech/tags/safety.md>), [software-engineering](<https://devfeed.tech/tags/software-engineering.md>)

### AI overview

OpenAI introduces GPT-6 Astra, a model positioned for computer use, coding, cybersecurity, science, and professional work. The article highlights alignment evaluations, benchmark results, availability through ChatGPT and cloud/API channels, and simulated computer-use performance versus GPT-5.6 Sol.

### Source excerpt

Introducing GPT-6 Astra, our most intelligent and aligned model yet, with state-of-the-art capabilities across computer use, coding, cybersecurity, and science.

## 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 LLMs: How They're Built

DevFeed: [Granite 4.2 LLMs: How They're Built](<https://devfeed.tech/articles/granite-4-2-llms-how-they-re-built-7257.md>)

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

Author: Yousaf Shah; Swanand Kadhe; Riddhiman Moulick; Ashish Sunil Agrawal; Santosh Borse

Published: 2026-08-25T15:14:14Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Language models](<https://devfeed.tech/topics/language-models.md>), [AI Chat](<https://devfeed.tech/topics/ai-chat.md>), [AI-assisted coding](<https://devfeed.tech/topics/ai-assisted-coding.md>), [releases](<https://devfeed.tech/topics/releases.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agents](<https://devfeed.tech/tags/agents.md>), [apache](<https://devfeed.tech/tags/apache.md>), [chain-of-thought](<https://devfeed.tech/tags/chain-of-thought.md>), [code](<https://devfeed.tech/tags/code.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [grouped-query-attention](<https://devfeed.tech/tags/grouped-query-attention.md>), [ibm](<https://devfeed.tech/tags/ibm.md>), [llms](<https://devfeed.tech/tags/llms.md>), [models](<https://devfeed.tech/tags/models.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [sglang](<https://devfeed.tech/tags/sglang.md>), [tool](<https://devfeed.tech/tags/tool.md>), [training](<https://devfeed.tech/tags/training.md>), [transformer-architecture](<https://devfeed.tech/tags/transformer-architecture.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

### AI overview

Granite 4.2 is a family of 3B, 8B, and 30B dense decoder-only reasoning language models. The article covers their training pipeline, thinking modes, native tool calling, and agentic reinforcement learning for the 8B and 30B models.

### Source excerpt

Authors: Granite Team, IBM TL;DR: Granite 4.2 is our first family of dense, decoder-only reasoning LLMs, released in three sizes: 3B, 8B, and 30B. These models are post-trained from Granite-4.1 base models. Granite-4.1 base models were pre-trained from scratch on roughly 15T tokens with a five-phase strategy that extends the context window to 512K tokens, supervised fine-tuned on chain-of-thought, reasoning, and agentic-trajectory data, then post-trained with a multi-stage reinforcement...

## 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.

## Pacing model development in an era of cyber-critical capabilities

DevFeed: [Pacing model development in an era of cyber-critical capabilities](<https://devfeed.tech/articles/pacing-model-development-in-an-era-of-cyber-critical-capabilities-6599.md>)

Original publisher: [Read original article](<https://openai.com/index/pacing-model-development-cyber-capabilities>)

Published: 2026-08-18T11:00:00Z

Content type: article

Language: en

Sources: [OpenAI News](<https://devfeed.tech/sources/openai-news.md>)

Topics: [AI Chat](<https://devfeed.tech/topics/ai-chat.md>), [ai observability](<https://devfeed.tech/topics/ai-observability.md>), [incident](<https://devfeed.tech/topics/incident.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [company](<https://devfeed.tech/tags/company.md>), [cybersecurity](<https://devfeed.tech/tags/cybersecurity.md>), [frontier-ai-models](<https://devfeed.tech/tags/frontier-ai-models.md>), [incident](<https://devfeed.tech/tags/incident.md>), [model-development](<https://devfeed.tech/tags/model-development.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [security](<https://devfeed.tech/tags/security.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

OpenAI says it temporarily slowed frontier-model scaling and paused reinforcement-learning training while strengthening monitoring, alignment, security, red-teaming, and evaluation safeguards for increasingly capable AI systems.

### Source excerpt

OpenAI is strengthening monitoring, alignment, and security for frontier AI models. See how new safeguards are guiding the pace of model development.

## Grok 4.6: 500K Context, Pricing, and Agentic Coding Features

DevFeed: [Grok 4.6: 500K Context, Pricing, and Agentic Coding Features](<https://devfeed.tech/articles/what-s-new-in-grok-4-6-from-500k-context-to-pricing-16517.md>)

Original publisher: [Read original article](<https://appwrite.io/blog/post/whats-new-in-grok-46-from-500k-context-to-pricing>)

Author: Aishwari Pahwa

Published: 2026-08-13T00:00:00Z

Content type: article

Language: en

Sources: [Appwrite Blog](<https://devfeed.tech/sources/appwrite-blog.md>)

Topics: [Frontier Model](<https://devfeed.tech/topics/frontier-model.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [coding](<https://devfeed.tech/topics/coding.md>), [API](<https://devfeed.tech/topics/api.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [api](<https://devfeed.tech/tags/api.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [coding](<https://devfeed.tech/tags/coding.md>), [context-window](<https://devfeed.tech/tags/context-window.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [frontier-model](<https://devfeed.tech/tags/frontier-model.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [token](<https://devfeed.tech/tags/token.md>), [web-development](<https://devfeed.tech/tags/web-development.md>)

### AI overview

This article describes Grok 4.6, focusing on long-running agents, coding, interactive and visual work, its 500K-token context window, pricing, benchmarks, and API availability. It also outlines claimed training changes, longer task trajectories, and self-testing or verification during multi-step work.

### Source excerpt

Grok 4.6 is SpaceXAI's frontier model for long-running agents and coding. See full benchmarks, token pricing, the 500K context window, and API setup.

## 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.

## 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

## 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....

## The grader is the reward: What we learned from reinforcement fine-tuning on Azure Foundry

DevFeed: [The grader is the reward: What we learned from reinforcement fine-tuning on Azure Foundry](<https://devfeed.tech/articles/the-grader-is-the-reward-what-we-learned-from-reinforcement-fine-tuning-on-azure-foundry-32262.md>)

Original publisher: [Read original article](<https://medium.com/data-science-at-microsoft/the-grader-is-the-reward-what-we-learned-from-reinforcement-fine-tuning-on-azure-foundry-16a6bbd1ac11?source=rss----a6e43238cdaf---4>)

Author: Moid Hassan

Published: 2026-07-28T07:16:01Z

Content type: article

Language: en

Sources: [Data Science at Microsoft](<https://devfeed.tech/sources/data-science-at-microsoft.md>)

Topics: [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [Microsoft](<https://devfeed.tech/topics/microsoft.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [azure-foundry](<https://devfeed.tech/tags/azure-foundry.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [data-scientist](<https://devfeed.tech/tags/data-scientist.md>), [deep-reinforcement](<https://devfeed.tech/tags/deep-reinforcement.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [microsoft](<https://devfeed.tech/tags/microsoft.md>), [model](<https://devfeed.tech/tags/model.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>)

### AI overview

This article describes using reinforcement fine-tuning on Azure Foundry to improve language-model-generated business communications. It explains that prompting a frontier model produced generic drafts with poor context handling, excessive length, repeated questions, and occasional unsupported details, motivating efforts to teach the model what quality means for a specific domain.

### Source excerpt

Where this story starts Continue reading on Data Science + AI at Microsoft "

## 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.

## NVIDIA Vera CPU: Olympus Cores Built for Maximum Single-Thread Performance in Agentic AI

DevFeed: [NVIDIA Vera CPU: Olympus Cores Built for Maximum Single-Thread Performance in Agentic AI](<https://devfeed.tech/articles/nvidia-vera-cpu-olympus-cores-built-for-maximum-single-thread-performance-in-agentic-ai-6865.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/inside-nvidia-vera-cpu-olympus-cores-built-for-maximum-single-threaded-performance-in-agentic-ai/>)

Author: Praveen Menon

Published: 2026-07-21T18:00:00Z

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: [cpu](<https://devfeed.tech/topics/cpu.md>)

Tags: [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai-factory](<https://devfeed.tech/tags/ai-factory.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [code](<https://devfeed.tech/tags/code.md>), [concurrency](<https://devfeed.tech/tags/concurrency.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [data-analytics](<https://devfeed.tech/tags/data-analytics.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [featured](<https://devfeed.tech/tags/featured.md>), [latency](<https://devfeed.tech/tags/latency.md>), [memory](<https://devfeed.tech/tags/memory.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [nvidia-vera](<https://devfeed.tech/tags/nvidia-vera.md>), [performance](<https://devfeed.tech/tags/performance.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [sandboxes](<https://devfeed.tech/tags/sandboxes.md>), [tools](<https://devfeed.tech/tags/tools.md>), [top-stories](<https://devfeed.tech/tags/top-stories.md>), [vera-cpu](<https://devfeed.tech/tags/vera-cpu.md>), [vera-rubin](<https://devfeed.tech/tags/vera-rubin.md>)

### AI overview

The article explains how NVIDIA's Vera CPU and its Olympus cores are designed for the single-threaded performance, memory bandwidth, and predictable latency required by concurrent agentic AI workloads.

### Source excerpt

Agentic AI shifts more of the critical execution path onto the CPU. Agents operate in sandboxes to execute code, invoke tools, retrieve context, interact with...

## Introducing Real World VoiceEQ: Measuring the human quality of voice AI

DevFeed: [Introducing Real World VoiceEQ: Measuring the human quality of voice AI](<https://devfeed.tech/articles/introducing-real-world-voiceeq-measuring-the-human-quality-of-voice-ai-7454.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/real-world-voiceeq>)

Author: David Ayllon; Alice; Jeff Brooks; Franc Camps Febrer; Jakub Piotr Cłapa; Theo Lebryk; Jens Madsen; Olya Ossipova; Sharath Rao; Hoon Shin

Published: 2026-07-15T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [voice ai](<https://devfeed.tech/topics/voice-ai.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Human-AI evaluation](<https://devfeed.tech/topics/human-ai-evaluation.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [asr](<https://devfeed.tech/topics/asr.md>), [human feedback](<https://devfeed.tech/topics/human-feedback.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [announcement](<https://devfeed.tech/tags/announcement.md>), [audio](<https://devfeed.tech/tags/audio.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [human-feedback](<https://devfeed.tech/tags/human-feedback.md>), [leaderboard](<https://devfeed.tech/tags/leaderboard.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [speech](<https://devfeed.tech/tags/speech.md>), [voice-ai](<https://devfeed.tech/tags/voice-ai.md>)

### AI overview

Real World VoiceEQ is a benchmark for evaluating the human quality of voice AI beyond latency and word error rate. It measures how voice systems recognize, produce, and respond to acoustic information such as tone, emotion, speaker identity, and background context across ASR, TTS, speech-to-speech, and speech understanding. The benchmark covers more than 40 voice models, 15+ evaluation dimensions, and more than 60 metrics, using over 1 million human ratings.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

[Next page](<https://devfeed.tech/tags/reinforcement-learning.md?cursor=WyIyMDI2LTA3LTE1VDAwOjAwOjAwKzAwOjAwIiwgIjE4NzE2MzE4LTRkYjktNDMyNy1iZjcwLTBhNzQyNWE1YjIyNSJd>)