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.

Jul 13Week of Sep 28

Latest papers 402

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MDForge: Agentic Molecular Dynamics Pipeline Design under Sparse Simulator Feedback
Jun 11, 2026Zehong Wang, Yijun Ma, Connor R. Schmidt +7AI Agents for Scientific Discovery Molecular Dynamics Simulation
University of Notre Dame · University of Connecticut
Hybrid Open-Ended Tri-Evolution Makes Better Deep Researcher
Jun 10, 2026Hongming Piao, Chi Liu, Mengzhuo Chen +5Deep Research Agents LLM Agent Training
IQuest Research · Zhejiang University
Orchestra-o1: Omnimodal Agent Orchestration
Jun 10, 2026Fan Zhang, Vireo Zhang, Shengju Qian +8LLM Agent Orchestration Group Relative Policy Optimization
CUHK · LIGHTSPEED · PKU +2
HERO: Hindsight-Enhanced Reflection from Environment Observations for Agentic Self-Distillation
Jun 10, 2026Haoran Liu, Yuwei Zhang, Xiyao Li +2Credit Assignment in RL On-Policy Distillation
University of California, San Diego · Independent Researcher · University of California, Berkeley
INFRAMIND: Infrastructure-Aware Multi-Agent Orchestration
Jun 9, 2026Ahasan Kabir, Jiaqi Xue, Mengxin Zheng +1Multi-Agent System Optimization Multi-Agent Orchestration
University of Central Florida
Effective Reinforcement Learning for Agentic Search by Recycling Zero-Variance Queries During Training
Jun 9, 2026João Coelho, João Magalhães, Bruno Martins +1Reinforcement Learning Group Relative Policy Optimization
Language Technologies Institute, Carnegie Mellon University, United States · Instituto Superior Técnico and INESC-ID, University of Lisbon, Portugal · NOVA LINCS, NOVA School of Science and Technology, Portugal
HIPIF: Hierarchical Planning and Information Folding for Long-Horizon LLM Agent Learning
Jun 9, 2026Juncheng Diao, Zhicong Lu, Peiguang Li +6Hierarchical RL Long-Horizon LLM Agents
1Meituan · University of Chinese Academy of Sciences
3SPO: State-Score-Supervised Policy Optimization for LLM Agents
Jun 8, 2026Yu Han, Kailing Li, Yang Jiao +4Credit Assignment in RL Step-Level Credit Assignment in RL
School of Computer Science and Technology, East China Normal University · Fudan University · KAUST +1
AliyunConsoleAgent: Training Web Agents in Real-World Cloud Environments via Distillation and Reinforcement Learning
Jun 8, 2026Bojie Rong, Zheyu Shen, Qiaoping Wang +7Cloud Computing LLM Agent Training
Alibaba Cloud China
Co-Evolving Skill Generation and Policy Optimization
Jun 7, 2026Zhiwei Zhang, Yudi Lin, Nikki Lijing Kuang +4LLM Agent Skill Learning RL for LLM Agents
The Pennsylvania State University · Nanyang Technological University · University of California, San Diego +2
From Player to Master: Enhancing Test-Time Learning of LLM Agents via Reinforcement Learning over Memory
Jun 7, 2026Yishuo Cai, Xingyu Guo, Xuancheng Huang +8Continual Learning for LLM Agents LLM Agent Memory
Peking University, Beijing, China · Central South University, Changsha, China · Zhipu AI, Beijing, China +3
Self-evolving LLM agents with in-distribution Optimization
Jun 5, 2026Yudi Zhang, Meng Fang, Zhenfang Chen +1Reinforcement Learning Process Reward Models
Eindhoven University of Technology, Netherlands · University of Liverpool, United Kingdom · MIT-IBM Watson AI Lab.
SCALE: Scalable Cross-Attention Learning with Extrapolation for Agentic Workflow Scheduling
Jun 5, 2026Zhifei Xu, Jierui Lan, Zixuan Liang +2RL for LLM Agents
Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, China
When Denser Credit Is Not Enough: Evidence-Calibrated Policy Optimization for Long-Horizon LLM Agent Training
Jun 4, 2026Yuanfan Li, Qi Zhou, Wenjing Duan +1Credit Assignment in RL Step-Level Credit Assignment in RL
X-LANCE Lab, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China · Faculty of Electronic and Information Engineering, Xi’an Jiaotong University
Agentic Monte Carlo: Simulating Reinforcement Learning for Black-Box Agents
Jun 3, 2026Dae Yon Hwang, Raunaq Suri, Valentin Villecroze +4Reinforcement Learning Bayesian RL
Layer 6 AI, Toronto, Canada.
Policy-Conditioned Counterfactual Credit for Verifiable Reinforcement Learning of Long-Horizon Language Agents
Jun 3, 2026Renwei MengCredit Assignment in RL Long-Horizon Agent Evaluation
Ahu
Learning While Acting: A Skill-Enhanced Test-Time Co-Evolution Framework for Online Lifelong Learning Agents
Jun 3, 2026Bo Mao, Jie Zhou, Yutao Yang +5Lifelong Learning Agents LLM Agent Skill Learning
School of Computer Science and Technology, East China Normal University · Shanghai AI Laboratory · Software Engineering Institute, East China Normal University
Scaling Self-Evolving Agents via Parametric Memory
Jun 3, 2026Tao Ren, Weiyao Luo, Hui Yang +8RL for LLM Agents Self-Evolving Agents
Qwen-Character Team · Alibaba Group · Peking University
Trading Human Curation for Synthetic Augmentation in RLVR
Jun 2, 2026Akshansh, Leonardo Rosa Rodrigues, Michael Korostelev +2Synthetic Data Augmentation Reinforcement Learning with Verifiable Rewards
Pareto AI
Multi $^2$ : Hierarchical Multi-Agent Decision-Making with LLM-Based Agents in Interactive Environments
Jun 2, 2026Sangeun Park, Minhae KwonMulti-Agent LLM Systems Hierarchical RL
Department of Electrical and Computer Engineering, Sungkyunkwan University, Republic of Korea.
InfoMem: Training Long-Context Memory Agents with Answer-Conditioned Information Gain
Jun 2, 2026Tiancheng Han, Yong Li, Wuzhou Yu +2Long-Context Modeling RL for LLM Agents
1Tongji University · 2Shanghai Innovation Institute · 3Shanghai AI Laboratory
ARBOR: Online Process Rewards via a Reusable Rubric Buffer for Search Agents
Jun 2, 2026Zheng Liu, Longxiang Zhang, Xintong Wang +8Reward Modeling Process Reward Models
Tsinghua University · Alibaba Group · Peking University +1
Policy and World Modeling Co-Training for Language Agents
Jun 1, 2026Ning Lu, Baijiong Lin, Shengcai Liu +9Agentic RL World Model Learning
Southern University of Science and Technology · Hong Kong University of Science and Technology · Hong Kong University of Science and Technology (Guangzhou) +2
Harness-1: Reinforcement Learning for Search Agents with State-Externalizing Harnesses
Jun 1, 2026Pengcheng Jiang, Zhiyi Shi, Kelly Hong +5Reinforcement Learning Agentic Retrieval
University of Illinois at Urbana-Champaign · †UC Berkeley · ✧Chroma
SIRI: Self-Internalizing Reinforcement Learning with Intrinsic Skills for LLM Agent Training
Jun 1, 2026Zhongyu He, Yuanfan Li, Fei Huang +9LLM Agent Skill Learning Long-Horizon LLM Agents
Xiamen University · Meituan · Macao Polytechnic University
ReSkill: Reconciling Skill Creation with Policy Optimization in Agentic RL
Jun 1, 2026Zelin He, Haotian Lin, Boran Han +6Reinforcement Learning LLM Agent Skill Learning
The Pennsylvania State University · † Work done during an internship at Amazon · Amazon IntelliHub +1
HomeFlow: A Data Flywheel for Smart Home Agent Training with Verifiable Simulation
May 31, 2026Yi Gu, Huacan Wang, Shuo Zhang +10AI Agent Benchmarks LLM Agent Training
1Midea Group · 2Beijing University of Posts and Telecommunications · 3Donghua University +1
Learning to Retrieve: Dual-Level Long-Term Memory for Text-to-SQL Agents
May 30, 2026Yibo Wang, Nikki Lijing Kuang, Philip S. Yu +2Text-to-SQL Memory-Augmented Agents
University of Illinois Chicago · Snowflake AI Research
Skill Reuse as Compression in Agentic RL
May 29, 2026Zhikun Xu, Yu Feng, Jacob Dineen +3Agentic RL Minimum Description Length
Arizona State University · University of Pennsylvania · University of Southern California
Task-Focused Memorization for Multimodal Agents
May 29, 2026Tao Zou, Yichen He, Tian Qiu +2Continual Learning for LLM Agents Multimodal Memory
ByteDance Seed · Fudan University

Jun 11, 2026, cs.AIJ/K move · Enter open · S save

MDForge: Agentic Molecular Dynamics Pipeline Design under Sparse Simulator Feedback

Zehong Wang, Yijun Ma, Connor R. Schmidt, Tianyi Ma+6

University of Notre Dame · University of Connecticut

AI Agents for Scientific Discovery Molecular Dynamics Simulation Physics-Based Simulation RL for LLM Agents

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Molecular dynamics (MD) is the canonical in-silico method for atomistic molecular science, simulating molecular behavior from first-principle physics. Designing an MD pipeline for a new system requires substantial expert knowledge: running it on even one molecule is expensive, ruling out trial-and-error. We automate this expert pipeline-design process with an LLM agent. Unlike existing MD agents that orchestrate a predefined tool set, we treat pipeline design as open-ended code generation in which the agent's behavior is reshaped online by verbal reward. Specifically, we build MDForge, an LLM agent whose in-context update rule densifies the sparse reward via a multi-agent debate among physics experts. On three SAMPL host-guest binding free-energy benchmarks, MDForge automatically designs MD pipelines competitive with human experts. Deployed on a library of unseen candidate guests, its CB[7] pipeline discovers a novel binder that wet-lab competition NMR confirms is a high-affinity, picomolar CB[7] binder. Our data and code are available at https://github.com/Zehong-Wang/MDForge.

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Latest papers 402