LLM-Guided RL

RL: Reinforcement Learning

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8 papers in the last four weeks, up 60% on the four weeks before. 0.1% of all new papers.

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Latest papers 76

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RHyVE: Competence-Aware Verification and Phase-Aware Deployment for LLM-Generated Reward Hypotheses
Apr 30, 2026Feiyu Wu, Xu Zheng, Zhuocheng Wang +2Reinforcement Learning LLM-Guided RL
School of Cyber Engineering, Xidian University
Bridging Reasoning and Action: Hybrid LLM-RL Framework for Efficient Cross-Domain Task-Oriented Dialogue
Apr 25, 2026Yangyang Zhao, Linfan Dai, Li Cai +2Constrained RL LLM-Guided RL
School of Computer Science and Technology, Changsha University of Science and Technology · School of Computer Science and Engineering, Central South University · Text Computing and Cognitive Intelligence MOE Engineering Research Center, Guizhou University +2
Self-Guided Plan Extraction for Instruction-Following Tasks with Goal-Conditional Reinforcement Learning
Apr 22, 2026Zoya Volovikova, Nikita Sorokin, Dmitriy Lukashevskiy +2Reinforcement Learning LLM-Guided RL
1AXXX · 2MIRAI
Scaling Self-Play with Self-Guidance
Apr 22, 2026Luke Bailey, Kaiyue Wen, Kefan Dong +2Self-Play RL Automated Theorem Proving
Stanford University
Do LLM-derived graph priors improve multi-agent coordination?
Apr 19, 2026Nikunj Gupta, Rajgopal Kannan, Viktor PrasannaMulti-Agent Coordination LLM-Guided RL
University of Southern California, Los Angeles, California, United States · DEVCOM ARL Army Research Oﬀice, Los Angeles, California, United States
GRAIL: Autonomous Concept Grounding for Neuro-Symbolic Reinforcement Learning
Apr 18, 2026Hikaru Shindo, Henri Rößler, Quentin Delfosse +1Reinforcement Learning LLM-Guided RL
Technical University of Darmstadt · Google Intrinsic · German Research Center for Artificial Intelligence (DFKI) +1
Generalizable Dense Reward for Long-Horizon Robotic Tasks
Mar 31, 2026Silong Yong, Stephen Sheng, Carl Qi +6Reinforcement Learning Long-Horizon Robotic Manipulation
Carnegie Mellon University · Amazon Robotics · UT Austin
DriveVLM-RL: Neuroscience-Inspired Reinforcement Learning with Vision-Language Models for Safe and Deployable Autonomous Driving
Mar 18, 2026Zilin Huang, Zihao Sheng, Zhengyang Wan +4VLMs for Autonomous Driving LLM-Guided RL
Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI, 53706, USA · Lyles School of Civil and Construction Engineering, Purdue University, West Lafayette, IN 47907, USA · Department of Civil Engineering, McGill University, Montreal, QC, H3A 0C3, Canada
LLM-Guided Reinforcement Learning for Audio-Visual Speech Enhancement
Mar 14, 2026Chih-Ning Chen, Jen-Cheng Hou, Hsin-Min Wang +3LLM-Guided RL Speech Enhancement
Department of Electrical Engineering, National Taiwan University, Taiwan · Research Center for Information Technology Innovation, Academia Sinica, Taiwan · Center for Hearing Research, University of California Irvine, USA
Novelty Adaptation Through Hybrid Large Language Model (LLM)-Symbolic Planning and LLM-guided Reinforcement Learning
Mar 11, 2026Hong Lu, Pierrick Lorang, Timothy R. Duggan +2Symbolic Planning LLM-Guided RL
Department of Computer Science, Tufts University, Medford, MA, USA · Austrian Institute of Technology GmbH, Vienna, Austria
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
Don't Tell the Answer, Truly Guide the Reasoning During RL Rollouts
Oct 10, 2025Xinyi Wang, Jinyi Han, Zishang Jiang +7Reinforcement Learning RL for Language Model Reasoning
School of Data Science, Fudan University · 2Shanghai Institute of Artificial Intelligence for Education, East China Normal University · College of Computer Science and Artificial Intelligence, Fudan University +1
CLARity: Reasoning Consistency Alone Can Teach Reinforced Experts
Oct 10, 2025Jiuheng Lin, Cong Jiang, Zirui Wu +2RL for Language Models RL for Language Model Reasoning
Wangxuan Institute of Computer Technology, Peking University
Selective Expert Guidance for Effective and Diverse Exploration in Reinforcement Learning of LLMs
Oct 5, 2025Zishang Jiang, Jinyi Han, Tingyun Li +7RL for Language Model Reasoning LLM-Guided RL
School of Data Science, Fudan University · Shanghai Institute of Artificial Intelligence for Education, East China Normal University · College of Computer Science and Artificial Intelligence, Fudan University +1
Self-CriTeach: LLM Self-Teaching and Self-Critiquing for Improving Robotic Planning via Automated Domain Generation
Sep 25, 2025Jinbang Huang, Zhiyuan Li, Yuanzhao Hu +4Large Language Model-Based Robot Planning Synthetic Data Generation for Robotics
Huawei Noah’s Ark Lab · University of Toronto · University of British Columbia +1
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

Apr 30, 2026, cs.AIJ/K move · Enter open · S save

RHyVE: Competence-Aware Verification and Phase-Aware Deployment for LLM-Generated Reward Hypotheses

Feiyu Wu, Xu Zheng, Zhuocheng Wang, Yi ming Dai+1

School of Cyber Engineering, Xidian University

Reinforcement Learning LLM-Guided RL Robotic RL Reward Design for RL

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Large language models (LLMs) make reward design in reinforcement learning substantially more scalable, but generated rewards are not automatically reliable training objectives. Existing work has focused primarily on generating, evolving, or selecting reward candidates, while paying less attention to when such candidates can be verified and deployed during policy optimization. We study this deployment-time problem by treating generated rewards as reward hypotheses whose utility depends on the competence of the current policy and the phase of training. We propose \textsc{RHyVE}, a competence-aware verification and phase-aware deployment protocol that compares small sets of reward hypotheses from shared policy checkpoints using short-horizon fork verification. Our experiments show that reward rankings are unreliable at low competence but become informative after task-dependent thresholds. On a sparse manipulation task, phase-aware deployment improves peak and retained performance under a locked protocol. Updated LLM-generated reward-candidate experiments show candidate-family-dependent behavior: generated pools can exhibit phase-dependent winner changes, but no fixed warm-up schedule is universally optimal. Held-out schedule selection, conservative selector baselines, compute-matched controls, and scale controls further show that \textsc{RHyVE} is best understood as a verification-informed deployment protocol rather than a universal scheduler. Dense and all-failure boundary experiments delimit the scope of the method. Together, these results suggest that reward generation and reward deployment should be studied as coupled problems: generated rewards must be verified and deployed under changing policy competence.

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