Agentic RL

RL: Reinforcement Learning

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28 papers in the last four weeks, up 300% on the four weeks before. 0.3% of all new papers.

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Self-Induced Outcome Potential: Turn-Level Credit Assignment for Agents without Verifiers
May 6, 2026Senkang Hu, Yong Dai, Xudong Han +4Agentic RL Reinforcement Learning
Hong Kong JC STEM Lab of Smart City · City University of Hong Kong · University of Oxford +4
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.
AEM: Adaptive Entropy Modulation for Multi-Turn Agentic Reinforcement Learning
May 1, 2026Haotian Zhao, Songlin Zhou, Yuxin Zhang +9Agentic RL Credit Assignment in RL
Baidu · Tsinghua University · Fudan University
Rethinking Agentic Reinforcement Learning In Large Language Models
Apr 30, 2026Fangming Cui, Ruixiao Zhu, Cheng Fang +2RL for Language Models Agentic RL
Beijing, China · Shanghai, China
FutureWorld: A Live Reinforcement Learning Environment for Predictive Agents with Real-World Outcome Rewards
Apr 29, 2026Zhixin Han, Yanzhi Zhang, Chuyang Wei +11Agentic RL Reinforcement Learning
College of Software, Nankai University · Zhongguancun Academy, Beijing, China · Academy of Mathematics and Systems Science, Chinese Academy of Sciences +3
AgenticQwen: Training Small Agentic Language Models with Dual Data Flywheels for Industrial-Scale Tool Use
Apr 23, 2026Yuanjie Lyu, Chengyu Wang, Haonan Zheng +4Tool-Augmented Language Model Agents Agentic RL
Alibaba Group, Hangzhou, China
DR-Venus: Towards Frontier Edge-Scale Deep Research Agents with Only 10K Open Data
Apr 21, 2026Venus Team, Sunhao Dai, Yong Deng +10Agentic RL Deep Research Agents
Ant Group
StepPO: Step-Aligned Policy Optimization for Agentic Reinforcement Learning
Apr 20, 2026Daoyu Wang, Qingchuan Li, Mingyue Cheng +4Agentic RL Credit Assignment in RL
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China
LiteResearcher: A Scalable Agentic RL Training Framework for Deep Research Agent
Apr 20, 2026Wanli Li, Bince Qu, Bo Pan +5Agentic RL Deep Research Agents
Zhejiang University · Simplex AI · The Hong Kong Polytechnic University
TrafficClaw: A Generalizable LLM Agent in the Unified Physical Environment for Urban Traffic Control
Apr 19, 2026Siqi Lai, Pan Zhang, Yuping Zhou +3Agentic RL Language Model-Based Control
The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China · Shandong University, Jinan, Shandong, China
GrandCode: Achieving Grandmaster Level in Competitive Programming via Agentic Reinforcement Learning
Apr 3, 2026DeepReinforce Team, Xiaoya Li, Guoyin Wang +3RL for Code Generation Agentic RL
DeepReinforce Team
ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning
Feb 25, 2026Xiaoxuan Wang, Han Zhang, Haixin Wang +11Agentic RL Reinforcement Learning
Department of Computer Science, University of California, Los Angeles, California, USA · University of Wisconsin–Madison, Madison, Wisconsin, USA
Pushing Forward Pareto Frontiers of Proactive Agents with Behavioral Agentic Optimization
Feb 11, 2026Yihang Yao, Zhepeng Cen, Haohong Lin +6Agentic RL LLM Agents
Carnegie Mellon University · Salesforce AI Research · Massachusetts Institute of Technology +2
CLEANER: Self-Purified Trajectories Boost Agentic Reinforcement Learning
Jan 21, 2026Tianshi Xu, Yuteng Chen, Meng LiAgentic RL Reinforcement Learning
Peking University · NTU, Singapore
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
OpenTinker: Separating Concerns in Agentic Reinforcement Learning
Jan 12, 2026Siqi Zhu, Jiaxuan YouAgentic RL LLM Agent Training
University of Illinois Urbana-Champaign
LLM-Empowered Agentic MAC Protocols: A Dynamic Stackelberg Game Approach
Oct 13, 2025Renxuan Tan, Rongpeng Li, Fei Wang +4Agentic RL Stackelberg Games
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China · Huawei Technologies Company Ltd., Shanghai 210026, China · Zhejiang Lab as well as Zhejiang University, Hangzhou 310027, China +1
DeepTravel: An End-to-End Agentic Reinforcement Learning Framework for Autonomous Travel Planning Agents
Sep 26, 2025Yansong Ning, Rui Liu, Jun Wang +6Agentic RL LLM Agent Training
The Hong Kong University of Science and Technology (Guangzhou) · Didichuxing Co. Ltd
AgentFly: Scaling Agentic Reinforcement Learning with Unified Resource System
Jul 20, 2025Renxi Wang, Rifo Ahmad Genadi, Bilal El Bouardi +5Agentic RL LLM Agent Training
Mohamed bin Zayed University of Artificial Intelligence
Agentic Episodic Control
Jun 2, 2025Xidong Yang, Wenhao Li, Junjie Sheng +4Agentic RL Reinforcement Learning
East China Normal University · Tongji University · Shanghai Jiao Tong University +2

May 6, 2026, cs.LGJ/K move · Enter open · S save

Self-Induced Outcome Potential: Turn-Level Credit Assignment for Agents without Verifiers

Senkang Hu, Yong Dai, Xudong Han, Zhengru Fang+3

Hong Kong JC STEM Lab of Smart City · City University of Hong Kong · University of Oxford+4

Agentic RL Reinforcement Learning Reward Shaping RL for Language Model Reasoning Step-Level Credit Assignment in RL Step-Level Credit Assignment+1

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Long-horizon LLM agents depend on intermediate information-gathering turns, yet training feedback is usually observed only at the final answer, because process-level rewards require high-quality human annotation. Existing turn-level shaping methods reward turns that increase the likelihood of a gold answer, but they require answer supervision or stable task-specific verifiers. Conversely, label-free RL methods extract self-signals from output distributions, but mainly at the answer or trajectory level and therefore cannot assign credit to intermediate turns. We propose Self-Induced Outcome Potential (SIOP), which treats semantic clusters of final answers as latent future outcome states for potential-based turn-level credit assignment. For each query, SIOP samples multiple rollouts, clusters final answers into semantic outcome modes, and builds a reliability-aware target distribution over these states. It then rewards turns for increasing posterior support for reliable future states using a tractable cluster-level approximation. The objective generalizes information-potential shaping from gold-answer supervision to settings without task-specific gold verifiers while avoiding the broadcasted rollout-level advantages used by standard GRPO. We formalize the framework, characterize its supervised gold-answer limit, and show that SIOP improves average performance over verifier-free outcome-level baselines on seven search-augmented agentic reasoning benchmarks while approaching a gold-supervised outcome baseline. Code is available at https://github.com/dl-m9/SIOP.git.

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