RL Post-Training

RL: Reinforcement Learning

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Overview

Reinforcement learning post-training studies how to adapt pretrained language, multimodal, and action models using rewards from people, verifiers, evaluators, or environments. Common approaches include reinforcement learning from human feedback and verifiable rewards, with policy-gradient methods such as PPO and GRPO, alongside preference optimization, self-distillation, offline replay, and adaptive curricula. Work also develops critics and step-level credit assignment for long-horizon agents, as well as systems that make rollout generation and training more efficient. A central tension is improving task performance while preserving exploration, concise outputs, prior capabilities, and reliable generalization when feedback is sparse or imperfect.

Latest papers

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426 papers89 papers in the last four weeks, up 134% on the four weeks before. 0.5% of all new papers.

  1. Reinforcement Learning with Verifiable yet Noisy Rewards under Imperfect Verifiers

    Oct 1, 2025Xin-Qiang Cai, Wei Wang, Feng Liu +3Reinforcement LearningRL for Language Model Reasoning

    RIKEN AIP, Tokyo, Japan · The University of Tokyo, Tokyo, Japan · The University of Melbourne, Melbourne, Australia

  2. ReST-RL: Reinforcing LLM Reasoning through Unified Self-Training and Value-Guided Search

    Aug 27, 2025Sining Zhoubian, Dan Zhang, Jie TangRL for Code GenerationRL for Language Model Reasoning

    Department of Computer Science and Technology, Tsinghua University · Work done when interned at Z.ai. · School of Electronics and Computer Scinence, Univeristy of Southampton

  3. Post-Training Large Language Models via Reinforcement Learning from Self-Feedback

    Jul 29, 2025Carel van Niekerk, Renato Vukovic, Benjamin Ruppik +3RL for Language ModelsRL for Language Model Reasoning

    Heinrich Heine University Düsseldorf, Germany

  4. AsyncFlow: An Asynchronous Streaming RL Framework for Efficient LLM Post-Training

    Jul 2, 2025Zhenyu Han, Ansheng You, Haibo Wang +16RL for Language ModelsAsynchronous RL

    Huawei · Individual Researcher

  5. GVPO: Group Variance Policy Optimization for Large Language Model Post-Training

    Apr 28, 2025Kaichen Zhang, Yuzhong Hong, Junwei Bao +4LLM Post-TrainingLLM Alignment

    Thrust of Artificial Intelligence, Hong Kong University of Science and Technology (Guangzhou) · Zuoyebang Education Technology · Department of Computer Science and Engineering, HKUST

  6. Reinforcement Learning from Human Feedback

    Apr 16, 2025Nathan LambertLLM AlignmentDirect Preference Optimization