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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Reinforcement Learning for Computer-Use Agents with Autonomous Evaluation
Jun 23, 2026Marta Sumyk, Oleksandr KosovanRL for GUI Agents RL Fine-Tuning
Ukrainian Catholic University, Lviv, Ukraine
SQLConductor: Search-to-Policy Learning for Step-wise Text-to-SQL Orchestration
Jun 22, 2026Yizhang Zhu, Zhangyang Peng, Boyan Li +1Text-to-SQL RL for Tool Use
HKUST(GZ) Guangzhou, China
Self-Evolution for Multi-Turn Tool-Calling Agents via Divergence-Point Preference Learning
Jun 22, 2026Jiaqiang TangTool-Using Agents Self-Improving Agents
The Hong Kong University of Science and Technology (Guangzhou)
RODS: Reward-Driven Online Data Synthesis for Multi-Turn Tool-Use Agents
Jun 17, 2026Ruishan Fang, Siyuan Lu, Chenyi Zhuang +1Synthetic Data Generation Sample-Efficient RL
Inclusion AI, Ant Group · Zhejiang University · Westlake University +1
PACT: Privileged Trace Co-Training for Multi-Turn Tool-Use Agents
Jun 15, 2026Zhenbang Du, Jun Luo, Zhiwei Zheng +8Learning from Demonstration LLM Agent Training
Georgia Institute of Technology · Ohio State University · University of Pennsylvania +1
Towards Pareto-Optimal Tool-Integrated Agents with Pareto Ranking Policy Optimization
Jun 15, 2026Junyi Li, Xiaowei Qian, Yingyi Zhang +6Multi-Objective Reinforcement Learning Multi-Objective Language Model Alignment
Department of Data Science, City University of Hong Kong, Hong Kong, China · Zhejiang University of Technology, Hangzhou, China · Huawei Technologies Noah’s Ark Lab, Hong Kong, China
CacheRL:Multi-Turn Tool-Calling Agents via Cached Rollouts and Hybrid Reward
Jun 12, 2026Md Amirul Islam, Sumiran Thakur, Huancheng Chen +3LLM Agent Training Tool-Using Language Agents
Center for Advanced AI, Accenture
SENTINEL: Failure-Driven Reinforcement Learning for Training Tool-Using Language Model Agents
Jun 11, 2026Ziyi Wang, Yuxuan Lu, Yimeng Zhang +8AI Agent Reliability Reinforcement Learning
Northeastern University · Independent Researcher · Northwestern University
Keep Policy Gradient in Charge: Sibling-Guided Credit Distillation for Long-Horizon Tool-Use Agents
Jun 10, 2026Tianyu Ding, Jianhong Xin, Juan Pablo De la Cruz WeinsteinToken-Level Credit Assignment Group Relative Policy Optimization
Amazon Web Services
APPO: Agentic Procedural Policy Optimization
Jun 10, 2026Xucong Wang, Ziyu Ma, Yong Wang +5Agentic RL Credit Assignment in RL
University of Science and Technology of China · AMAP, Alibaba Group · Southern University of Science and Technology
IAPO: Input Attribution-Aware Policy Optimization for Tool Use in Small Multimodal Agents
Jun 10, 2026Yifan Yang, Zhen Zhang, Jiayi Tian +2Tool Use in VLMs Reinforcement Learning
University of California, Santa Barbara
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
Capability-Aligned Hierarchical Learning for Tool-Augmented LLMs
Jun 8, 2026Haotong Yang, Ting Long, Yi ChangHierarchical RL LLM Agent Planning
Jilin University
SlimSearcher: Training Efficiency-Aware Web Agents via Adaptive Reward Gating
Jun 5, 2026Zequn Xie, Junjie Wang, Dan Yang +4Reward Shaping LLM Agent Training
Zhejiang University
Exploring Agentic Tool-Calling Decisions via Uncertainty-Aligned Reinforcement Learning
Jun 5, 2026Yijin Zhou, Linqian Zeng, Xiaoya Lu +4Reinforcement Learning LLM Uncertainty Estimation
Shanghai Jiao Tong University, China · Shanghai Artificial Intelligence Laboratory, China · Shanghai Innovation Institute, China
TAPO: Tool-Aware Policy Optimization via Credit Transfer for Multimodal Search Agents
Jun 4, 2026Chengqi Dong, Chuhuai Yue, Hang He +6Multimodal IR Token-Level Credit Assignment
University of Science and Technology of China · 2Meituan
SCI-PRM: A Tool Aware Process Reward Model for Scientific Reasoning Verification
Jun 3, 2026Xiangyu Zhao, Henry Hengyuan Zhao, Yiheng Wang +7Process Reward Models Scientific Reasoning
The Hong Kong Polytechnic University · National University of Singapore · Shanghai AI Lab +3
Synthesize and Reward -- Reinforcement Learning for Multi-Step Tool Use in Live Environments
Jun 2, 2026Ibrahim Abdelaziz, Asim Munawar, Kinjal Basu +4Synthetic Data Generation Reinforcement Learning with Verifiable Rewards
IBM Research
Tool-Aware Optimization with Entropy Guidance for Efficient Agentic Reinforcement Learning
Jun 2, 2026Hongye Cao, Nuo Yan, Haoyuan Deng +5Agentic RL RL for Language Model Reasoning
National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China · Nanyang Technological University, Singapore · China Mobile NineVerse Artificial Intelligence Technology (Beijing) Co., Ltd., and Institute of Artificial Intelligence, NineVerse, Beijing 100032, China
RUBAS: Rubric-Based Reinforcement Learning for Agent Safety
Jun 2, 2026Xian Qi Loye, Qinglin Su, Zhexin Zhang +5Reinforcement Learning AI Agent Safety
The Conversational AI (CoAI) group, DCST, Tsinghua University · Huawei Noah’s Ark Lab
Learning When Not to Act: Mitigating Tool Abuse in Agentic Reinforcement Learning
Jun 1, 2026Liuji Chen, Dianxing Tang, Xing Shi +4Agentic RL RL for Language Model Reasoning
1NLPR, Institute of Automation, Chinese Academy of Sciences · 3Zhejiang University · 2ByteDance
MetaForge: A Self-Evolving Multimodal Agent that Retrieves, Adapts, and Forges Tools On Demand
Jun 1, 2026Shouang Wei, Houcheng Min, Xinpeng Dong +8LLM Agent Skill Learning Multimodal Agents
East China Normal University · Zhejiang University · Tsinghua University +2
SPADER: Step-wise Peer Advantage with Diversity-Aware Exploration Rewards for Multi-Answer Question Answering
May 30, 2026Qiming Shi, Zhaolu Kang, Yunfan Zhou +2Credit Assignment in RL Step-Level Credit Assignment in RL
State Key Lab of CAD&CG, Zhejiang University · School of Software and Microelectronics, Peking University · School of Software Technology, Zhejiang University
On Effectiveness and Efficiency of Agentic Tool-calling and RL Training
May 28, 2026Tong Liu, Cheng Qian, Matej Cief +4Tool-Use Evaluation LLM Agent Evaluation
1LMU Munich & Munich Center for Machine Learning · 2UIUC · 3Amazon +1
DeepTool: Scaling Interleaved Deliberation in Tool-Integrated Reasoning via Process-Supervised Reinforcement Learning
May 28, 2026Yang He, Xiao Ding, Bibo Cai +5Reinforcement Learning RL for Language Model Reasoning
Research Center for Social Computing and Interactive Robotics, Harbin Institute of Technology, China
unix-ctf: Procedural Environments for Unix-Competence Reinforcement Learning
May 27, 2026Geoffrey Bradway, Roger Creus Castanyer, Lorenz Wolf +3AI Agent Benchmarks LLM Agent Training
Vmax
Agent Explorative Policy Optimization for Multimodal Agentic Reasoning
May 27, 2026Minki Kang, Shizhe Diao, Ryo Hachiuma +4Multimodal Reasoning Agentic Reasoning
1NVIDIA · 2KAIST
Efficient Agentic Reinforcement Learning with On-Policy Intrinsic Knowledge Boundary Enhancement
May 26, 2026Dingwei Chen, Zefang Zong, Zhipeng Ma +5Agentic RL LLM Agent Training
Tencent Inc · The Chinese University of Hong Kong · Shenzhen MSU-BIT University
Mind the Tool Failures: Achieving Synergistic Tool Gains for Medical Agents
May 26, 2026Yunhui Gan, Tan Pan, Kaiyu Guo +5Tool Use RL for Tool Use
Fudan University · Shanghai Academy of Artificial Intelligence for Science · Shanghai Innovation Institute +2
Peak-Then-Collapse and the Four Interface Channels of Knowledge-Graph Tool Use
May 25, 2026Tianda Sun, Dimitar KazakovTool-Use Evaluation Reinforcement Learning
University of York · Department of Computer Science · Heslington, York +1

Jun 23, 2026, cs.AIJ/K move · Enter open · S save

Reinforcement Learning for Computer-Use Agents with Autonomous Evaluation

Marta Sumyk, Oleksandr Kosovan

Ukrainian Catholic University, Lviv, Ukraine

RL for GUI Agents RL Fine-Tuning RL for Tool Use

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Computer-Use Agents (CUAs) execute high-level user goals by perceiving and acting directly within graphical user interfaces. However, reinforcement learning for CUAs remains difficult because open-ended desktop environments rarely provide scalable, machine-readable reward signals: task success is often visually grounded and hard to specify with handcrafted reward functions or dense manual labels. We propose an RL fine-tuning framework that uses autonomous vision-language evaluation as a scalable supervision signal for GUI agents. Given a final screenshot and the original instruction, a Vision-Language Model judges task completion and provides terminal feedback without task-specific heuristics or manual labels during policy optimization. Because autonomous evaluators are imperfect, we model their feedback as a noisy binary reward channel and derive a noise-corrected reward estimator for Proximal Policy Optimization. Experiments across macOSWorld, Windows Agent Arena, and OSWorld show that corrected evaluator rewards outperform both zero-shot baselines and raw evaluator rewards, improving success rates by an average of 12.6 percentage points over zero-shot performance and 5.1 points over raw evaluator fine-tuning. These results suggest that autonomous evaluation can serve as a practical reward signal for RL in GUI environments when evaluator noise is explicitly modeled and corrected.

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