Advances in Reinforcement Learning for Large Language Models

The field of large language models (LLMs) is moving towards more advanced reinforcement learning techniques to improve their reasoning capabilities. Recent studies have shown that techniques such as RLVR, GRPO, and PPO can significantly improve the performance of LLMs on various tasks, including math, science, and code-related problems. One of the key challenges in this area is the ability of LLMs to incorporate external feedback and adapt to new environments. Researchers are exploring various methods to address this challenge, including the use of guidance, self-verification, and iterative feedback.

Notable papers in this area include SAGE, which introduces a new approach for specification-aware grammar extraction using large language models, and Agent-RLVR, which proposes a framework for training software engineering agents via guidance and environment rewards. ReVeal is another notable paper that introduces a multi-turn reinforcement learning framework for self-evolving code agents.

These studies demonstrate the potential of reinforcement learning to advance the field of LLMs and improve their ability to reason and adapt to new situations.

Sources

SAGE:Specification-Aware Grammar Extraction for Automated Test Case Generation with LLMs

Agent-RLVR: Training Software Engineering Agents via Guidance and Environment Rewards

ReVeal: Self-Evolving Code Agents via Iterative Generation-Verification

Feedback Friction: LLMs Struggle to Fully Incorporate External Feedback

Adaptive Guidance Accelerates Reinforcement Learning of Reasoning Models

Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs

Knowledge Adaptation as Posterior Correction

GenerationPrograms: Fine-grained Attribution with Executable Programs

Reasoning with Exploration: An Entropy Perspective

Semantically-Aware Rewards for Open-Ended R1 Training in Free-Form Generation

Lessons from Training Grounded LLMs with Verifiable Rewards

AutoRule: Reasoning Chain-of-thought Extracted Rule-based Rewards Improve Preference Learning

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