RL for LLM Agents

RL: Reinforcement Learning · LLM: Large Language Model

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70 papers in the last four weeks, up 141% on the four weeks before. 0.7% of all new papers.

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SkillGraph: Skill-Augmented Reinforcement Learning for Agents via Evolving Skill Graphs
May 12, 2026Xiaoyuan Li, Moxin Li, Keqin Bao +4LLM Agent Skill Learning LLM Agent Skill Retrieval
University of Science and Technology of China · National University of Singapore · Alibaba Group
GEAR: Granularity-Adaptive Advantage Reweighting for LLM Agents via Self-Distillation
May 12, 2026Sijia Li, Yuchen Huang, Zifan Liu +7RL for Language Model Reasoning Credit Assignment in RL
Hong Kong University of Science and Technology · Microsoft Research Asia
Agent-BRACE: Decoupling Beliefs from Actions in Long-Horizon Tasks via Verbalized State Uncertainty
May 12, 2026Joykirat Singh, Zaid Khan, Archiki Prasad +5Partially Observable RL Bayesian Inference
UNC Chapel Hill · Microsoft Research · The University of Texas at Austin
RubricEM: Meta-RL with Rubric-guided Policy Decomposition beyond Verifiable Rewards
May 11, 2026Gaotang Li, Bhavana Dalvi Mishra, Zifeng Wang +9Reinforcement Learning Rubric-Based RL
University of Illinois Urbana-Champaign · Google Cloud AI Research
Evolving-RL: End-to-End Optimization of Experience-Driven Self-Evolving Capability within Agents
May 11, 2026Zhiyuan Fan, Wenwei Jin, Feng Zhang +4Reinforcement Learning Language Model Self-Improvement
1Xiaohongshu Inc. · School of Computer Science, Peking University
DeepRefine: Agent-Compiled Knowledge Refinement via Reinforcement Learning
May 11, 2026Haoyu Huang, Jiaxin Bai, Shujie Liu +6Knowledge Editing Knowledge Augmentation for Language Models
1HKUST · 2HKBU · 3Microsoft Research Asia
Verifiable Process Rewards for Agentic Reasoning
May 11, 2026Huining Yuan, Zelai Xu, Huaijie Wang +6Process Reward Models Credit Assignment in RL
Tsinghua University
Skill-R1: Agent Skill Evolution via Reinforcement Learning
May 10, 2026Yash Vishe, Rohan Surana, Xunyi Jiang +8Agent Skill Learning RL for LLM Agents
UC San Diego · Adobe Research
PiCA: Pivot-Based Credit Assignment for Search Agentic Reinforcement Learning
May 10, 2026Dongyi Liu, Yifan Niu, Qinwen Wang +2Reinforcement Learning Reward Shaping
The Hong Kong University of Science and Technology (Guangzhou) · The Hong Kong University of Science and Technology
Learning to Explore: Scaling Agentic Reasoning via Exploration-Aware Policy Optimization
May 9, 2026Xingyuan Hua, Sheng Yue, Ju RenRL for Language Model Reasoning Test-Time Scaling
Department of Computer Science and Technology, Tsinghua University, Beijing, China · State Key Laboratory of Internet Architecture, Tsinghua University, Beijing, China · School of Cyber Science and Technology, Sun Yat-sen University Shenzhen Campus, Shenzhen, China
EvoMAS: Learning Execution-Time Workflows for Multi-Agent Systems
May 9, 2026Chengdong Xu, Kaiqiang Ke, Ziheng Liu +4Multi-Agent LLM Systems Multi-Agent System Optimization
Sun Yat-Sen University · ShanghaiTech University · Zhejiang University +1
SkillMaster: Toward Autonomous Skill Mastery in LLM Agents
May 9, 2026Min Yang, Jinghua Piao, Xu Xia +4LLM Agent Self-Improvement LLM Agent Skill Learning
Shandong University · Zhongguancun Academy · Tsinghua University +2
Iterative Critique-and-Routing Controller for Multi-Agent Systems with Heterogeneous LLMs
May 9, 2026Wenzhi Fang, Liangqi Yuan, Guangchen Lan +2Multi-Agent LLM Systems Multi-Agent Coordination
Purdue University · Yonsei University
Towards Autonomous Business Intelligence via Data-to-Insight Discovery Agent
May 8, 2026Dongming Wu, Junwen Li, Ming Lu +2AI-Assisted Decision Making RL for LLM Agents
1Rajax Network Technology(Taobao Shangou of Alibaba)
MedExAgent: Training LLM Agents to Ask, Examine, and Diagnose in Noisy Clinical Environments
May 8, 2026Yicheng Gao, Xiaolin Zhou, Yahan Li +2Healthcare Medical Diagnosis
University of Southern California · Arizona State University
PACEvolve++: Improving Test-time Learning for Evolutionary Search Agents
May 7, 2026Minghao Yan, Bo Peng, Benjamin Coleman +11Test-Time Adaptation Reinforcement Learning
1Google · University of Wisconsin–Madison · 3Google DeepMind
SkillOS: Learning Skill Curation for Self-Evolving Agents
May 7, 2026Siru Ouyang, Jun Yan, Yanfei Chen +13Continual Learning for LLM Agents LLM Agent Skill Learning
University of Illinois Urbana-Champaign · Google Cloud AI Research · Massachusetts Institute of Technology
Weblica: Scalable and Reproducible Training Environments for Visual Web Agents
May 7, 2026Oğuzhan Fatih Kar, Roman Bachmann, Yuanzheng Gong +2LLM Agent Training RL for LLM Agents
Apple
A $^2$ TGPO: Agentic Turn-Group Policy Optimization with Adaptive Turn-level Clipping
May 7, 2026Dingwei Chen, Zefang Zong, Zhipeng Ma +5Credit Assignment in RL Policy Gradient Methods
Tencent Inc · The Chinese University of Hong Kong · Shenzhen MSU-BIT University
Skill1: Unified Evolution of Skill-Augmented Agents via Reinforcement Learning
May 7, 2026Yaorui Shi, Yuxin Chen, Zhengxi Lu +6LLM Agent Skill Learning LLM Agent Skill Retrieval
University of Science and Technology of China · 2Meituan · 3National University of Singapore +2
Milestone-Guided Policy Learning for Long-Horizon Language Agents
May 7, 2026Zixuan Wang, Yuchen Yan, Hongxing Li +7Credit Assignment in RL Long-Horizon Agent Tasks
Zhejiang University · Baidu Inc.
Selective Rollout: Mid-Trajectory Termination for Multi-Sample Agent RL
May 7, 2026Zhiyuan Zhai, Xin WangReinforcement Learning Group Relative Policy Optimization
Fudan University
From History to State: Constant-Context Skill Learning for LLM Agents
May 6, 2026Haoyang Xie, Xinyuan Wang, Yancheng Wang +2LLM Agent Skill Learning LLM Agent Training
School of Computing and Augmented Intelligence, Arizona State University
OpenSearch-VL: An Open Recipe for Frontier Multimodal Search Agents
May 6, 2026Shuang Chen, Kaituo Feng, Hangting Chen +7Multimodal IR Multimodal Search Agents
Tencent Hunyuan · University of California, Los Angeles · The Chinese University of Hong Kong +1
Uno-Orchestra: Parsimonious Agent Routing via Selective Delegation
May 6, 2026Zhiqing Cui, Haotong Xie, Jiahao Yuan +11Multi-Agent System Optimization Multi-Agent Orchestration
Project lead.
Reinforcement Learning for LLM-based Multi-Agent Systems through Orchestration Traces
May 4, 2026Chenchen ZhangMulti-Agent LLM Systems LLM Agent Orchestration
Independent Researcher
On Training Large Language Models for Long-Horizon Tasks: An Empirical Study of Horizon Length
May 4, 2026Sunghwan Kim, Junhee Cho, Beong-woo Kwak +6Long-Horizon Planning Length Generalization
Department of Artificial Intelligence, Yonsei University · †Work done during internship at Microsoft Research. · 2Microsoft Research.
T $^2$ PO: Uncertainty-Guided Exploration Control for Stable Multi-Turn Agentic Reinforcement Learning
May 4, 2026Haixin Wang, Hejie Cui, Chenwei Zhang +7Agentic RL Reinforcement Learning
University of California, Los Angeles · 2Amazon.com Inc.
Planner Matters! An Efficient and Unbalanced Multi-agent Collaboration Framework for Long-horizon Planning
May 4, 2026Wenyi Wu, Sibo Zhu, Kun Zhou +1Reinforcement Learning Multi-Agent Collaboration
University of California, San Diego
Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory
May 1, 2026Derong Xu, Shuochen Liu, Pengfei Luo +8Memorization in Language Models Memorization in Generative Models
University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence · City University of Hong Kong · Dalian University of Technology +1

May 12, 2026, cs.CLJ/K move · Enter open · S save

SkillGraph: Skill-Augmented Reinforcement Learning for Agents via Evolving Skill Graphs

Xiaoyuan Li, Moxin Li, Keqin Bao, Yubo Ma+3

University of Science and Technology of China · National University of Singapore · Alibaba Group

LLM Agent Skill Learning LLM Agent Skill Retrieval Graph-Based Agent Memory Agent Skill Learning RL for LLM Agents Memory-Augmented Language Model Agents

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Skill libraries enable large language model agents to reuse experience from past interactions, but most existing libraries store skills as isolated entries and retrieve them only by semantic similarity. This leads to two key challenges for compositional tasks. Firstly, an agent must identify not only relevant skills but also how they depend on and build upon each other. Secondly, it also makes library maintenance difficult, since the system lacks structural cues for deciding when skills should be merged, split, or removed. We propose SKILLGRAPH, a framework that represents reusable skills as nodes in a directed graph, with typed edges encoding prerequisite, enhancement, and co-occurrence relations. Given a new task, SKILLGRAPH retrieves not just individual skills, but an ordered skill subgraph that can guide multi-step decision making. The graph is continuously updated from agent trajectories and reinforcement learning feedback, allowing both the skill library and the agent policy to improve together. Experiments on ALFWorld, WebShop, and seven search-augmented QA tasks show that SKILLGRAPH achieves state-of-the-art performance against memory-augmented RL methods, with especially large gains on complex tasks that require composing multiple skills.

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Latest papers 402