# rlvr A reinforcement-learning training approach for language-model agents that uses automatically verifiable task outcomes as rewards. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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]() Original publisher: [Read original article]() Author: Linpo Guo Published: 2026-09-15T16:11:36Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Artificial Intelligence](), [Amazon SageMaker AI](), [Fine-tuning](), [rlvr](), [grpo](), [Inference](), [model-deployment](), [Reinforcement learning](), [SDK](), [Serverless]() Tags: [ai](), [amazon-sagemaker](), [amazon-sagemaker-ai](), [aws](), [compute](), [cost](), [customization](), [expert-400](), [fine-tuning](), [inference](), [jobs](), [reinforcement-learning](), [rlvr](), [sdk](), [technical-how-to]() ### 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. ## 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]() Original publisher: [Read original article]() Author: Chris Alexiuk Published: 2026-07-23T16:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [rlvr](), [Large Language Model](), [Fine-tuning](), [Reinforcement learning](), [Python](), [coding](), [math]() Tags: [agentic-ai-generative-ai](), [blog](), [coding](), [customization](), [developer-tools-techniques](), [developers](), [experiment](), [featured](), [fine-tuning](), [getting-started](), [math](), [nemotron](), [nvidia](), [open](), [python](), [reinforcement-learning](), [rlvr](), [top-stories](), [train](), [training](), [tutorial]() ### 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... ## Cursor's Agent-Written CUDA Kernels, Claude Cowork for Non-Engineers, and Stanford's Frontier Systems - 📚 The Tokenizer Edition #24 DevFeed: [Cursor's Agent-Written CUDA Kernels, Claude Cowork for Non-Engineers, and Stanford's Frontier Systems - 📚 The Tokenizer Edition #24]() Original publisher: [Read original article]() Author: Sairam Sundaresan Published: 2026-04-16T12:03:16Z Content type: article Language: en Sources: [Gradient Ascent]() Topics: [Artificial Intelligence](), [CUDA](), [cursor](), [rlvr](), [multimodal](), [Large Language Model](), [Learning]() Tags: [ai](), [ai-ml](), [claude](), [cuda](), [cursor](), [llm](), [multimodal](), [rlvr](), [tutorial]() ### AI overview The Tokenizer Edition #24 is a curated AI/ML newsletter covering agent-written CUDA kernels at Cursor, RLVR training research, multimodal retrieval resources, AI infrastructure learning, and selected videos, tools, and papers. ### Source excerpt This week's most valuable AI resources ## Ecom-RLVE: Adaptive Verifiable Environments for E-Commerce Conversational Agents DevFeed: [Ecom-RLVE: Adaptive Verifiable Environments for E-Commerce Conversational Agents]() Original publisher: [Read original article]() Author: Rahul Bajaj; Jaya Nupur; Anuj Garg; ben burtenshaw Published: 2026-04-16T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [rlvr](), [openenv](), [Reinforcement learning](), [qwen](), [Large Language Model](), [AI Development](), [Open Source]() Tags: [agents](), [customer-service](), [e-commerce](), [llm](), [open-source](), [openenv](), [qwen](), [reinforcement-learning](), [research](), [rlvr](), [tools](), [training](), [workflows]() ### AI overview Ecom-RLVE introduces EcomRLVE-GYM, a suite of verifiable, multi-turn, tool-augmented e-commerce environments for training conversational agents. It uses procedural task generation, adaptive difficulty, and algorithmically verifiable rewards across shopping and customer-service workflows, with early results from training a Qwen 3 8B model using DAPO. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## TRL v1.0: Post-Training Library Built to Move with the Field DevFeed: [TRL v1.0: Post-Training Library Built to Move with the Field]() Original publisher: [Read original article]() Author: Quentin Gallouédec; Steven Liu; Pedro Cuenca; Sergio Paniego Published: 2026-03-31T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [trl](), [post-training](), [dpo](), [grpo](), [rlvr](), [Artificial Intelligence](), [Code]() Tags: [announcement](), [artificial-intelligence](), [code](), [dpo](), [grpo](), [open-source](), [post-training](), [reinforcement-learning](), [rlvr](), [software](), [trl](), [v1]() ### AI overview TRL v1.0 presents a post-training library designed to remain useful as algorithms, models, and paradigms change. The article explains the evolution from PPO to DPO-style preference optimization and RLVR methods such as GRPO, emphasizing flexible abstractions and practical comparison over a supposedly permanent design. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Reflections on AI at the end of 2025 DevFeed: [Reflections on AI at the end of 2025]() Original publisher: [Read original article]() Published: 2025-12-20T08:58:29Z Content type: opinion Language: en Sources: [Antirez]() Topics: [Artificial Intelligence](), [Large Language Model](), [Chain-of-thought](), [rlvr](), [AI-assisted coding](), [Programming]() Tags: [2025](), [ai](), [chain-of-thought](), [code](), [coding](), [coding-agents](), [reflections](), [reinforcement-learning]() ### AI overview An end-of-2025 reflection on developments in AI, including changing views of LLM representations, chain-of-thought, reinforcement learning with verifiable rewards, and growing adoption of AI-assisted programming. The author presents these as observations and expectations, including the possibility that improved reinforcement learning could become a major direction in AI. ### Source excerpt * For years, despite functional evidence and scientific hints accumulating, certain AI researchers continued to claim LLMs were stochastic parrots: probabilistic machines that would: 1. NOT have any representation about the meaning of the prompt. 2. NOT have any representation about what they were going to say. In 2025 finally almost everybody stopped saying so. * Chain of thought is now a fundamental way to improve LLM output. But, what is CoT? Why it improves output? I believe it is two things: 1. Sampling in the model representations (that is, a form of internal search). After information and concepts relevant to the prompt topic is in the context window, the model can better reply. 2. But if you mix this to reinforcement learning, the model also learns to put one token after the other (each token will change the model state) in order to converge to some useful reply. * The idea that scaling is limited to the number of tokens we have, is no longer true, because of reinforcement learning with verifiable rewards. We are still not at AlphaGo move 37 moment, but is this really impossible in the future? There are certain tasks, like improving a given program for speed, for instance, where in theory the model can continue to make progress with a very clear reward signal for a very long time. I believe improvements to RL applied to LLMs will be the next big thing in AI. * Programmers resistance to AI assisted programming has lowered considerably. Even if LLMs make mistakes, the ability of LLMs to deliver useful code and hints improved to the point most skeptics started to use LLMs anyway: now the return on the investment is acceptable for many more folks. The programming world is still split among who uses LLMs as colleagues (for instance, all my interaction is via the web interface of Gemini, Claude, ...), and who uses LLMs as independent coding agents. * A few well known AI scientists believe that what happened with Transformers can happen again, and better, following d ## Codex is Open Sourcing AI models DevFeed: [Codex is Open Sourcing AI models]() Original publisher: [Read original article]() Author: ben burtenshaw; shaun smith Published: 2025-12-11T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [codex](), [hugging face](), [AI-assisted coding](), [Fine-tuning](), [Open Source](), [Language models](), [rlvr](), [Deployment]() Tags: [agents](), [claude](), [codex](), [coding-agents](), [community](), [fine-tuning](), [gemini](), [how-to](), [hugging-face](), [llm](), [open-source](), [reinforcement-learning](), [training](), [tutorial]() ### AI overview This tutorial explains how to use Hugging Face Skills with OpenAI Codex to automate end-to-end machine learning experiments. Codex can fine-tune and align language models, monitor training metrics, evaluate checkpoints, create reports, deploy models locally, and publish them to the Hugging Face Hub. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## We Got Claude to Fine-Tune an Open Source LLM DevFeed: [We Got Claude to Fine-Tune an Open Source LLM]() Original publisher: [Read original article]() Author: ben burtenshaw; shaun smith Published: 2025-12-04T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [Claude Code](), [Fine-tuning](), [hugging face](), [jobs](), [GPU](), [lora](), [rlvr](), [codex]() Tags: [agents](), [claude](), [claude-code](), [codex](), [community](), [fine-tuning](), [gemini](), [gpu](), [hugging-face](), [llm](), [lora](), [open-source](), [reinforcement-learning](), [training]() ### AI overview This tutorial explains how Hugging Face Skills enable Claude Code to fine-tune language models by validating datasets, selecting GPUs, configuring authentication, submitting cloud training jobs, monitoring progress, and publishing finished models to the Hugging Face Hub. It covers LoRA, full fine-tuning, supervised fine-tuning, direct preference optimization, reinforcement learning with verifiable rewards, GGUF conversion, and multi-stage training pipelines. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Grading Complex Interactive Coding Programs with Reinforcement Learning DevFeed: [Grading Complex Interactive Coding Programs with Reinforcement Learning]() Original publisher: [Read original article]() Author: A Href; Allen Nie; Emma Brunskill; Chris Piech Published: 2022-03-28T07:00:00Z Content type: article Language: en Sources: [The Stanford AI Lab Blog]() Topics: [rlvr](), [Code Challenge](), [Artificial Intelligence](), [Programming](), [NeurIPS](), [browser]() Tags: [agents](), [algorithms](), [browser](), [coding](), [courses](), [games](), [machine-learning](), [neurips](), [programming](), [reinforcement-learning](), [speed]() ### AI overview This article presents the Play to Grade Challenge, which applies reinforcement-learning methods for game-playing AI agents to the automated grading of complex interactive coding assignments. It explains why games and interactive applications are difficult to evaluate automatically and describes the challenge introduced in a NeurIPS 2021 paper. ### Source excerpt [Summary] tl;dr: A tremendous amount of effort has been poured into training AI algorithms to competitively play games that computers have traditionally had trouble with, such as the retro games published by Atari, Go, DotA, and StarCraft II. The practical machine learning knowledge accumulated in developing these algorithms has paved the way for people to now routinely train game-playing AI agents for many games. Following this line of work, we focus on a specific category of games - those developed by students as part of a programming assignment. Can the same algorithms that master Atari games help us grade these game assignments? In our recent NeurIPS 2021 paper, we illustrate the challenges in treating interactive coding assignment grading as game playing and introduce the Play to Grade Challenge. Introduction Massive Online Coding Education has reached striking success over the past decade. Fast internet speed, improved UI design, code editors that are embedded in a browser window allow educational platforms such as Code.org to build a diverse set of courses tailored towards students of different coding experiences and interest levels (for example, Code.org offers "Star War-themed coding challenge," and "Elsa/Frozen themed for-loop writing"). As a non-profit organization, Code.org claims to have reached over 60 million learners across the world 1. Such organizations typically provide a variety of carefully constructed teaching materials such as videos and programming challenges. A challenge faced by these platforms is that of grading assignments. It is well known that grading is critical to student learning 2, in part because it motivates students to complete their assignments. Sometimes manual grading can be feasible in small settings, or automated grading used in simple settings such as when assignments are multiple choice or adopt a fill-in-the-blink modular coding structure. Unfortunately, many of the most exciting assignments, such as developing games or i