RL for Tool Use

RL: Reinforcement Learning

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17 papers in the last four weeks, up 42% on the four weeks before. 0.2% of all new papers.

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Latest papers 127

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SEAL: Synergistic Co-Evolution of Agents and Learning Environments
May 23, 2026Yihao Hu, Zhihao Wen, Xiujin Liu +3LLM Agent Training Self-Improving Agents
Ant Group · Westlake University · University of Michigan–Ann Arbor +1
B-GRTO: Bootstrapped Group Relative Tool Optimization for Referring Segmentation
May 22, 2026Mario Markov, Stefan Maria Ailuro, Mohammad Mahdi +2Image Segmentation Reinforcement Learning
INSAIT, Sofia University "St. Kliment Ohridski" · INSAIT, Sofia University “St. Kliment Ohridski”
ParaVT: Taming the Tool Prior Paradox for Parallel Tool Use in Agentic Video Reinforcement Learning
May 19, 2026Zuhao Yang, Kaichen Zhang, Sudong Wang +7Tool Use in VLMs Agentic RL
2NTU · 6LMMs-Lab · Work largely done during Z.Y., K.Z., S.W., and K.W.’s internship at MiroMind. +4
Are Tools Always Beneficial? Learning to Invoke Tools Adaptively for Dual-Mode Multimodal LLM Reasoning
May 19, 2026Qinghe Ma, Zhen Zhao, Yiming Wu +3Multimodal Large Language Models Multimodal Reasoning
The State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China · Shanghai Artificial Intelligence Laboratory, Shanghai, China · Shanghai Artificial Intelligence Laboratory +2
EnvFactory: Scaling Tool-Use Agents via Executable Environments Synthesis and Robust RL
May 18, 2026Minrui Xu, Zilin Wang, Mengyi DENG +12Tool-Augmented Language Model Agents Agentic RL
LARK, HKUST (GZ) · University of Cambridge · UCL +2
Implicit Hierarchical GRPO: Decoupling Tool Invocation from Execution for Tool-Integrated Mathematical Reasoning
May 18, 2026Li Wang, Xiaohan Wang, Xiaodong Lu +5RL for Language Model Reasoning Hierarchical RL
Meituan · Tsinghua University
Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs
May 17, 2026Yuxuan Lu, Ziyi Wang, Yingzhou Lu +12Tool-Augmented Language Model Agents Synthetic Data Generation
Northeastern University · Independent Researcher
TIER: Trajectory-Invariant Execution Rewards for Multi-Step Tool Composition
May 16, 2026Anay Kulkarni, ChiaEn Lu, Dheeraj Mekala +3Reward Modeling Reinforcement Learning with Verifiable Rewards
UC San Diego · Cisco Research
Case-Based Calibration of Adaptive Reasoning and Execution for LLM Tool Use
May 14, 2026Renning Pang, Tian Lan, Leyuan Liu +3Language Model Calibration LLM Tool Use
University of Electronic Science and Technology of China, Chengdu 611731, China
Prompting Policies for Multi-step Reasoning and Tool-Use in Black-box LLMs with Iterative Distillation of Experience
May 14, 2026Krishna Sayana, Ketan Todi, Ambarish JashRL for Language Models LLM Prompting
Google Research, Mountain View, CA
Reinforcement Learning for Tool-Calling Agents in Fast Healthcare Interoperability Resources (FHIR)
May 13, 2026Marius S. Knorr, Robert Müller, Jan P. Bremer +1Healthcare RL for Language Model Reasoning
IDM gGmbH, University Medical Center Hamburg-Eppendorf, Hamburg, Germany · Aganthos
ToolCUA: Towards Optimal GUI-Tool Path Orchestration for Computer Use Agents
May 12, 2026Xuhao Hu, Xi Zhang, Haiyang Xu +6Agentic RL RL for GUI Agents
Tongyi Lab, Alibaba Group · Fudan University · Shanghai Artificial Intelligence Laboratory
When Simulation Lies: A Sim-to-Real Benchmark and Domain-Randomized RL Recipe for Tool-Use Agents
May 12, 2026Xiaolin Zhou, Aojie Yuan, Zheng Luo +12Domain Randomization AI Agent Benchmarks
Arizona State University · University of Southern California · University of Pennsylvania +2
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
AHD Agent: Agentic Reinforcement Learning for Automatic Heuristic Design
May 9, 2026Haoze Lv, Ning Lu, Ziang Zhou +1Agentic RL Automated Heuristic Design
Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology · Zhongguancun Academy · The Hong Kong University of Science and Technology
CoCoDA: Co-evolving Compositional DAG for Tool-Augmented Agents
May 8, 2026Ziyang Yu, Qiyue Li, Liang ZhaoLLM Tool Use LLM Agent Training
1Emory University · 2Beijing Normal-Hong Kong Baptist University
Learning CLI Agents with Structured Action Credit under Selective Observation
May 8, 2026Haoyang Su, Ying WenComputer-Use Agents Credit Assignment in RL
Fudan University · Shanghai Innovation Institute · Shanghai Jiao Tong University
HyperEyes: Dual-Grained Efficiency-Aware Reinforcement Learning for Parallel Multimodal Search Agents
May 8, 2026Guankai Li, Jiabin Chen, Yi Xu +2Efficient Multimodal Inference Multimodal Search Agents
Xiaohongshu Inc. · University of Cambridge
Teaching Thinking Models to Reason with Tools: A Full-Pipeline Recipe for Tool-Integrated Reasoning
May 7, 2026Qianjia Cheng, Yuchen Zhang, Zhilin Wang +9RL for Language Model Reasoning RL for Tool Use
Zhejiang University · Shanghai AI Laboratory · Peking University +4
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
Every Step Counts: Step-Level Credit Assignment for Tool-Integrated Text-to-SQL
May 6, 2026Yaxun Dai, Baolin Sun, Junying Wang +6Credit Assignment in RL Text-to-SQL
Institute of Computer Science and Technology, Soochow University, China · Ant Digital Technologies, Ant Group · School of Management, University of Science and Technology of China
Maximizing Rollout Informativeness under a Fixed Budget: A Submodular View of Tree Search for Tool-Use Agentic Reinforcement Learning
May 6, 2026Yuelin Hu, Zhenbo Yu, Zhengxue Cheng +2Tree Search Group Relative Policy Optimization
Shanghai Jiao Tong University · Shanghai Maritime University
$S^3$ -R1: Learning to Retrieve and Answer Step-by-Step with Synthetic Data
May 2, 2026Harsh Goel, Akhil Udathu, Susmija Jabbireddy +2Multi-Hop QA Synthetic Data Generation
The University of Texas at Austin · Work done during an internship at Google DeepMind · Google DeepMind
Healthcare AI GYM for Medical Agents
May 1, 2026Minbyul JeongHealthcare On-Policy Distillation
Upstage AI
Democratizing Tool Learning with Environments Fully Simulated by a Free 8B Language Model
Apr 20, 2026Chenming Tang, Hsiu-Yuan Huang, Weijie Liu +3LLM Agent Training RL for Tool Use
♠National Key Laboratory for Multimedia Information Processing, Peking University · ♣School of Computer Science, Peking University · ∗Work done during an internship at Tencent +2
Does RL Expand the Capability Boundary of LLM Agents? A PASS@(k,T) Analysis
Apr 16, 2026Zhiyuan Zhai, Wenjing Yan, Xiaodan Shao +1LLM Agent Training RL for LLM Agents
Fudan University · Chinese University of Hong Kong · University of Waterloo
Agentic Tool Use in Large Language Models
Apr 1, 2026Jinchao Hu, Meizhi Zhong, Kehai Chen +2LLM Agent Evaluation Tool-Using Agents
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China · Independent Researcher, China
PhGPO: Pheromone-Guided Policy Optimization for Long-Horizon Tool Planning
Feb 14, 2026Yu Li, Guangfeng Cai, Shengtian Yang +5LLM-Guided RL Tool-Use Planning
School of Computer Science and Engineering, Southeast University, Nanjing, China · Huawei Noah’s Ark Lab, China
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 Reasoning Gradient Interference
School of Information, Renmin University of China, Beijing, China · Bytedance Inc., Beijing, China · Bytedance Inc., San Jose, USA
PaperScout: An Autonomous Agent for Academic Paper Search with Process-Aware Sequence-Level Policy Optimization
Jan 15, 2026Tingyue Pan, Jie Ouyang, Mingyue Cheng +7Agentic RL Policy Optimization
State Key Lab of Cognitive Intelligence, University of Science and Technology of China

May 23, 2026, cs.CLJ/K move · Enter open · S save

SEAL: Synergistic Co-Evolution of Agents and Learning Environments

Yihao Hu, Zhihao Wen, Xiujin Liu, Pan Wang+2

Ant Group · Westlake University · University of Michigan–Ann Arbor+1

LLM Agent Training Self-Improving Agents Adaptive Learning RL for Tool Use

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Large Language Model (LLM) agents are increasingly improved through interaction, yet most self-evolution methods adapt either the policy or the learning environment in isolation. We identify this structural gap as \emph{Agent-Environment Misalignment}: the agent's capability frontier changes during training, while the environment that provides supervision remains static or only weakly coupled to the agent's revealed failures. We propose SEAL, a closed-loop co-evolution framework for interactive tool-use agents. SEAL collects on-policy trajectories under executable verification, diagnoses failed rollouts into turn-level failure labels, and uses these diagnoses as a shared signal for both environment-side adaptation and model-side policy optimization. The environment evolves its training-time learning interface by exposing clearer tool affordance cues, constraint information, and recovery-oriented feedback, while the policy is updated with diagnosis-guided advantage reweighting. Extensive experiments across in-distribution and out-of-distribution multi-turn tool-use evaluations show that SEAL improves low-resource agent learning: with only 400 training samples, it yields +8.25 to +26.25 average-point gains across three backbones and exhibits positive out-of-distribution transfer. These results demonstrate the value of jointly adapting the learner and its training-time learning substrate for robust self-improving LLM agents.

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Latest papers 127