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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Ask the Expert: LLM-Guided Reinforcement Learning for Autonomous Cyber Defense
Oct 7, 2026Fernando Martinez, Abhishek Satyam, Tao Li +3Autonomous Cyber Defense LLM-Guided RL
Department of Computer and Information Sciences, Fordham University, New York, USA · Department of Systems Engineering, Stevens Institute of Technology, New Jersey, USA
An LLM-in-the-loop RL Framework for Bioinformatics Feature Selection
Oct 4, 2026Xinyuan Wang, Deepti Agrawal, Yanjie FuReinforcement Learning LLM-Guided RL
School of Computing and AI Arizona State University Tempe, USA
Iterative Policy Refinement through Semantic Rollout Analysis
Oct 1, 2026Feiyu Gavin Zhu, Qi Xu, Zhifei Deng +4Policy Optimization LLM-Guided RL
Carnegie Mellon University · Meta
Guide, Then Let Go: Gap-Adaptive Teacher Scheduling for Sparse-Reward Agentic RL
Sep 29, 2026Youling Huang, Tiankuo Xu, Jiaji Liu +10Agentic RL LLM-Guided RL
DUT · Kuaishou · XJTU +5
Agentic AI Networking for Heterogeneous Unmanned Aerial Systems in Low-Altitude Wireless Networks
Sep 17, 2026Nguyen Duc Minh Quang, Chang Liu, Shuangyang Li +1LLM-Guided RL Wireless Communications
School of Computing, Engineering, and Mathematical Sciences, La Trobe University, Melbourne, VIC, Australia · Chair of Communications and Information Theory, Technical University of Berlin, 10623 Berlin, Germany · School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, Australia
M $^3$ P-R1: Reinforcement Learning for Large Language Model Guided Multi-Modal Motion Planning via MIP Code Generation
Sep 16, 2026Xingpeng Sun, Zherong Pan, Kai Cheng +3Constrained Motion Planning Large Language Model-Based Robot Planning
Purdue University United States · LightSpeed Studios United States
Specifying Reward Functions for RL Without Environment Sampling
Sep 14, 2026Stephane Hatgis-Kessell, W. Bradley Knox, Emma BrunskillLLM-Guided RL Reward Design for RL
1Computer Science Department, Stanford University · 2Computer Science Department, The University of Texas at Austin
CanvasAnneal: Curriculum Reinforcement Learning for Diffusion Language Models
Sep 14, 2026Blake Olson, Yuhang Song, Emmett McQuinn +1RL for Language Model Reasoning Curriculum RL
Google DeepMind
Smart Adaptive Computing Across the Continuum: LLMs in IoT-Edge-Cloud Resource Management
Sep 10, 2026Antonino Vaccarella, Lanpei Li, Vincenzo Lomonaco +1Language Model-Based Control LLM-Guided RL
Institute of Information Science and Technologies “Alessandro Faedo” (ISTI), National Research Council of Italy (CNR), 56124 Pisa, Italy · Department of Computer Science, University of Pisa, 56127 Pisa, Italy · Department of AI, Data and Decision Sciences, LUISS University, 00197 Rome, Italy
Reinforcement Learning Enhanced LLM Agents for Complex Vehicle Routing Problems
Sep 1, 2026Yi Chen, Zikang Yu, Jiahai Wang +3Multi-Agent System Optimization LLM-Guided RL
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China · Department of Industrial Systems Engineering and Management, National University of Singapore, Singapore
From Solver Feedback to Faithful Plans: Multi-Role Reinforcement Learning for Symbolic Planning
Aug 22, 2026Chenghao Zhang, Yikai Mao, Haoyu Gao +4LLM Planning Symbolic Planning
University of Pennsylvania · Georgia Institute of Technology · Pace University +1
LoongReflect: Boosting Long-Horizon Reflection in Search Agents via Global Perspective Distillation
Aug 12, 2026Zhixin Zhang, Xinke Jiang, Zhibang Yang +5LLM-Guided RL LLM Agents
School of Computer Science, Peking University, Beijing, China · National Engineering Research Center for Software Engineering, Peking University, Beijing, China · Key Laboratory of High Confidence Software Technologies, Ministry of Education, Beijing, China +2
GRPO for Financial Advice Generation: Outperforming Commercial LLMs under CATE Evaluation
Aug 12, 2026Ofir Ben Shoham, Shrutendra Harsola, Vignesh Subrahmaniam +3AI-Assisted Decision Making LLM-as-a-Judge
Intuit
Mitigating Bus Bunching with Reinforcement Learning Enhanced by Semantic Stop Embedding
Aug 10, 2026Xin Dong, Vikash V. GayahRL Control LLM-Guided RL
Department of Civil and Environmental Engineering, The Pennsylvania State University University Park, PA, 16803
ProDVI: Programmatic Dynamics Priors for Value Network Initialization
Aug 6, 2026Xinwei Liu, Junyuan Liang, Jianting Zhang +1Reinforcement Learning LLM-Guided RL
1Sun Yat-sen University · 2Purdue University
Hybrid LLM-Augmented Reinforcement Learning Agents for Complex Sequential Decision Tasks
Aug 4, 2026Christophe D. Hounwanou, John Emeka Eze, Yaé Ulrich GabaLLM-Guided RL Long-Horizon LLM Agents
Department of Mathematics, AIMS, Kigali, Rwanda · African Institute for Mathematical Sciences (AIMS), Kigali, Rwanda · African Center for Advanced Studies, Pretoria, South Africa
PROGRESS: Coverage-guided RL to Train Search-augmented LLM Agent
Aug 2, 2026Sudipta Paul, Vijay Srinivasan, Vivek Kulkarni +4Reward Shaping LLM-Guided RL
AI Center-Mountain View, Samsung Electronics
Distilling Knowledge from Large Language Models into Lightweight Reinforcement Learning Agents for Autonomous Cyber Operations
Jul 30, 2026Konur Tholl, François Rivest, Mariam El Mezouar +2Autonomous Cyber Defense LLM-Guided RL
Department of Electrical and Computer Engineering, Royal Military College of Canada, Kingston, Canada · Department of Mathematics and Computer Science, Royal Military College of Canada, Kingston, Canada · Defence Research and Development Canada, Ottawa, Canada +1
LEACL: LLM-Enhanced Automatic Curriculum Learning for Reinforcement Learning in Long-Horizon Manipulation Tasks
Jul 26, 2026Faraz Heravi, James Ouyang, Zifan Xu +3LLM-Guided RL Robotic Manipulation
The University of Texas at Austin · Nanyang Technological University · Sony AI
Prompt-Driven Exploration
Jul 9, 2026Sunshine Jiang, John Marangola, David Zhang +6LLM-Guided RL RL Exploration
Massachusetts Institute of Technology · Improbable AI Lab · MIT-IBM Computing Research Lab
Open-ended Multi-agent Autocurricula via Visual Inspection of Policies with Multi-modal LLMs
Jul 9, 2026Lorenzo Pantè, Andrea Fanti, Roberto CapobiancoGame-Playing Agents Curriculum RL
Sapienza University of Rome, Italy · Sony AI, Zurich, Switzerland
TREK: Distill to Explore, Reinforce to Refine
Jul 6, 2026Yuanda Xu, Zhengze Zhou, Kayhan Behdin +10RL for Language Model Reasoning LLM-Guided RL
LinkedIn Corporation · Harvard University · Georgia Institute of Technology
Regime-Conditional Stabilisation of LLM-Augmented Cooperative Multi-Agent Reinforcement Learning
Jul 5, 2026Faid Keddouri, Sohaib Houhou, Aissa Boulmerka +1Reward Shaping LLM-Guided RL
National School of Artificial Intelligence (ENSIA), Sidi Abdellah Campus, Algiers, Algeria · Cosys-Grettia, Univ Gustave Eiffel, F-77454 Marne-la-Vallee, France.
ASK in the Dark: Uncertainty-Gated LLM Assistance under Partial Observability
Jul 2, 2026Juarez Monteiro, Nathan Gavenski, Guilherme Lima +3Partially Observable RL LLM Prompting
Kunumi Institute, Belo Horizonte, MG, Brazil · King’s College London, London, United Kingdom
Safe and Adaptive Cloud Healing: Verifying LLM-Generated Recovery Plans with a Neural-Symbolic World Model
Jul 2, 2026Junyan Tan, Haoran Lin, Siyuan Guo +4LLM-Based Program Synthesis LLM-Guided RL
Department of Computer Science Zhejiang University
Calibrating the Evaluator: Does Probability Calibration Mitigate Preference Coupling in LLM Agent Feedback Loops?
Jun 30, 2026Zewen LiuLLM-as-a-Judge Pairwise Preference Evaluation
Qilu Institute of Technology, School of Software Engineering Tai’an, Shandong, China
LaGO: Latent Action Guidance for Online Reinforcement Learning
Jun 23, 2026Kuan-Yen Liu, Ren-Jyun Huang, Ti-Rong WuLatent Action Learning LLM-Guided RL
Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign, USA · Department of Computer Science, National Yang Ming Chiao Tung University, Taiwan · Institute of Information Science, Academia Sinica, Taiwan.
Causal Reward World Models: Zero-shot Reward Design for Automated Skill Generation
Jun 22, 2026Yang Yang, Yuchuang Tong, Zhengtao Zhang +6RL for Robotics Robot Skill Learning
Institute of Automation, Chinese Academy of Sciences, Beijing 100049, China · Intelligent Manufacturing Institute, HFUT, Hefei 230051, China · School of Electrical Engineering and Automation, Anhui University, Hefei 230061, China +1
Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model
Jun 21, 2026Pengxiang Cai, Tianchen Fang, Xiaohan Li +3RL for Language Model Reasoning Curriculum RL
The Hong Kong University of Science and Technology (Guangzhou) · Nanyang Technological University · Zhejiang University
Hierarchical Control in Multi-Agent Games: LLM-based Planning and RL Execution
Jun 18, 2026Jannik Hösch, Alessandro Sestini, Florian Fuchs +6Sequential MARL Multi-Agent Coordination
1Electronic Arts (EA) · 2KTH Royal Institute of Technology, Stockholm, Sweden

Oct 7, 2026, cs.CRJ/K move · Enter open · S save

Ask the Expert: LLM-Guided Reinforcement Learning for Autonomous Cyber Defense

Fernando Martinez, Abhishek Satyam, Tao Li, Junaid Farooq+2

Department of Computer and Information Sciences, Fordham University, New York, USA · Department of Systems Engineering, Stevens Institute of Technology, New Jersey, USA

Autonomous Cyber Defense LLM-Guided RL Sample-Efficient RL Reward Shaping

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Policy-based reinforcement learning (RL) approaches have produced promising results for autonomous cyber defense; however, they are sample-inefficient in settings where defenders must respond under delayed, partial observations with actions from large action spaces. While large language models (LLMs) may reason semantically about security state space, high latency and trust assumptions prevent attractive in-line deployment models. We introduce Ask the Expert, a training-time guidance framework which first summarizes hard cyber-defense states, then intermittently queries an LLM for host-level defensive recommendations via a constrained action interface, and finally transforms those recommendations into tiered reward shaping for use with PPO. Because the LLM is discarded after training, deployment is a pure RL policy. Across TTCP CAGE CC1 and CC2 and both attacker types, this asymmetric design improves sample efficiency over PPO and outperforms the evaluated potential-based reward shaping (PBRS) baselines, while retaining the strongest terminal mean and requiring no LLM dependency at deployment time.

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