RL for LLM Agents

RL: Reinforcement Learning · LLM: Large Language Model

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Overview

This field studies how to train large language model agents to act over multiple steps in environments such as web browsers, coding tools, games, and simulated workplaces. Common approaches use policy-gradient methods such as PPO and GRPO, verifiable task rewards, and process-level credit assignment to connect outcomes to particular actions or turns. Other methods teach agents to plan hierarchically, manage memory and reusable skills, or coordinate multiple agents, often using simulators and environment-generated feedback. A central challenge is making learning stable and efficient when rewards are sparse and tasks are long, while ensuring gains transfer to new environments and do not come from exploiting flawed reward signals.

Latest papers

CardsList

616 papers134 papers in the last four weeks, up 139% on the four weeks before. 0.8% of all new papers.

  1. Learning to Remember: End-to-End Training of Memory Agents for Long-Context Reasoning

    Feb 13, 2026Kehao Zhang, Shangtong Gui, Sheng Yang +2Persistent Memory for Language ModelsLong-Context Inference

    1Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS) · University of Chinese Academy of Sciences, Beijing, China · 4Li Auto Inc. +1

  2. TSR: Trajectory-Search Rollouts for Multi-Turn RL of LLM Agents

    Feb 12, 2026Aladin Djuhera, Swanand Kadhe, Farhan Ahmed +3Tree SearchLLM Agent Training

    Technical University Munich · IBM Research

  3. FlexMARL: Graph-Driven Rollout-Training Co-Design for LLM-Based Multi-Agent Reinforcement Learning

    Feb 10, 2026Zhida Jiang, Zhaolong Xing, Jiawei Lu +13Multi-Agent Reinforcement LearningDistributed RL

    Huawei · Huazhong University of Science and Technology

  4. Dr. MAS: Stable Reinforcement Learning for Multi-Agent LLM Systems

    Feb 9, 2026Lang Feng, Longtao Zheng, Shuo He +2Multi-Agent LLM SystemsMulti-Agent System Optimization

    Nanyang Technological University, Singapore

  5. SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning

    Feb 9, 2026Peng Xia, Jianwen Chen, Hanyang Wang +10LLM Agent Skill LearningRL for LLM Agents

    UNC-Chapel Hill · University of Chicago · University of California San Diego +3

  6. Reasoning and Tool-use Compete in Agentic RL:From Quantifying Interference to Disentangled Tuning

    Feb 1, 2026Yu Li, Mingyang Yi, Xiuyu Li +6RL for Language Model ReasoningGradient Interference

    School of Information, Renmin University of China, Beijing, China · Bytedance Inc., Beijing, China · Bytedance Inc., San Jose, USA

  7. Just-In-Time Reinforcement Learning: Continual Learning in LLM Agents Without Gradient Updates

    Jan 26, 2026Yibo Li, Zijie Lin, Ailin Deng +5Continual Learning for LLM AgentsReinforcement Learning

    National University of Singapore, Singapore.

  8. CLEANER: Self-Purified Trajectories Boost Agentic Reinforcement Learning

    Jan 21, 2026Tianshi Xu, Yuteng Chen, Meng LiAgentic RLReinforcement Learning

    Peking University · NTU, Singapore

  9. PEARL: Self-Evolving Assistant for Time Management with Reinforcement Learning

    Jan 17, 2026Bingxuan Li, Jeonghwan Kim, Cheng Qian +4Long-Horizon Agent TasksRL for LLM Agents

    University of Illinois at Urbana-Champaign · Viven

  10. MemBuilder: Reinforcing LLMs for Long-Term Memory Construction via Attributed Dense Rewards

    Jan 9, 2026Zhiyu Shen, Ziming Wu, Fuming Lai +2Reinforcement LearningMemory-Augmented Language Models

    School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China · Tencent Inc.

  11. AdaSearch: Balancing Parametric Knowledge and Search in Large Language Models via Reinforcement Learning

    Dec 18, 2025Tzu-Han Lin, Wei-Lin Chen, Chen-An Li +3Retrieval-Augmented GenerationKnowledge Augmentation for Language Models

    National Taiwan University · Department of Computer Science, University of Virginia

  12. Iterative Policy Optimization: Batch-Online Multi-Turn RL via Single-Turn RL

    Nov 26, 2025Daniel R. Jiang, Ankur Samanta, Yukai Yang +3Reinforcement LearningPolicy Iteration

    Meta · Columbia University · FAIR at Meta

  13. Stabilizing Off-Policy Training for Long-Horizon LLM Agent via Turn-Level Importance Sampling and Clipping-Triggered Normalization

    Nov 25, 2025Chenliang Li, Adel Elmahdy, Alex Boyd +7RL for Language Model ReasoningImportance Sampling

    Texas A&M University · GE HealthCare · Independent Researcher +1

  14. Why Do LLM Agents Fail in Exploring New Environments? A World-Modeling Perspective

    Oct 16, 2025Shiqi Chen, Tongyao Zhu, Zian Wang +8RL ExplorationPolicy Learning

    City University of Hong Kong · National University of Singapore · The Hong Kong Polytechnic University +5

  15. There are Levels to It: Red Teaming Dialogue With Hierarchical Reinforcement Learning

    Aug 6, 2025Roman Belaire, Arunesh Sinha, Pradeep VarakanthamLLM Red TeamingAdversarial Attacks on LLMs

    Singapore Management University · Rutgers University

  16. Dynamic Optimizations of LLM Ensembles with Two-Stage Reinforcement Learning Agents

    Feb 6, 2025Selim Furkan Tekin, Gaowen Liu, Ramana Rao Kompella +1Multi-Agent LLM SystemsLanguage Model Ensembles

    Georgia Institute of Technology · Cisco Systems