RL Fine-Tuning

RL: Reinforcement Learning

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18 papers in the last four weeks, up 260% on the four weeks before. 0.2% of all new papers.

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EXPO-FT: Sample-Efficient Reinforcement Learning Finetuning for Vision-Language-Action Models
May 25, 2026Perry Dong, Kuo-Han Hung, Tian Gao +2Robot Skill Learning Robotic RL
Stanford University
Bilevel Optimization of Synthetic Trajectories for Multi-Turn LLM Fine-Tuning
May 23, 2026Shresth Verma, Mauricio Tec, Cheol Woo Kim +2Synthetic Data Augmentation Offline RL
Harvard University Cambridge, MA · Georgia Institute of Technology Atlanta, GA
CurveRL: Principled Distribution-Aware Context Reweighting for LLM Reasoning
May 23, 2026Ke Sun, Yizhou Zhao, Jiayi Xin +2Reinforcement Learning RL for Language Model Reasoning
University of Pennsylvania
Mechanistic origins of catastrophic forgetting: why RL preserves circuits better than SFT?
May 21, 2026Jeanmely Rojas Nunez, Viraj Sawant, Nathan Allen +4Supervised Fine-Tuning Mechanistic Interpretability
Algoverse AI Research · Independent
One-Way Policy Optimization for Self-Evolving LLMs
May 21, 2026Shuo Yang, Jinda Lu, Kexin Huang +6Policy Optimization Reinforcement Learning with Verifiable Rewards
1Shenzhen Graduate School, Peking Uni · 2Qwen Pilot Team · 2Qwen Pilot Team 3Dartmouth College +1
PREFINE: Preference-Based Implicit Reward and Cost Fine-Tuning for Safety Alignment
May 20, 2026Richa Verma, Bavish Kulur, Sanjay Chawla +1Direct Preference Optimization Constrained RL
TCS Research, Department of CSE, IIT Madras India · Department of Computing Science, University of Alberta Canada · Qatar Computing Research Institute, Hamad Bin Khalifa University Qatar +1
AGPO: Adaptive Group Policy Optimization with Dual Statistical Feedback
May 20, 2026Miaobo Hu, Shuhao Hu, Bokun Wang +5RL for Language Model Reasoning Group Relative Policy Optimization
Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100092, China · School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100049, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
General Preference Reinforcement Learning
May 18, 2026Muhammad Umer, Muhammad Ahmed Mohsin, Ahsan Bilal +5Multi-Objective Language Model Alignment RL Fine-Tuning
Stanford University · The University of Oklahoma
Beyond Inference-Time Search: Reinforcement Learning Synthesizes Reusable Solvers
May 18, 2026Soheyl Massoudi, Gabriel Apaza, Milad Habibi +1RL for Code Generation LLM-Based Program Synthesis
ETH Zürich Zürich, Switzerland · University of Maryland College Park, MD, USA
FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning
May 18, 2026Xikai Zhang, Yongzhi Li, Likang Xiao +6RL for Language Model Reasoning RL Exploration
Hangzhou International Innovation Institute, Beihang University · Kuaishou Technology · School of Artificial Intelligence, Beihang University
DyGRO-VLA: Cross-Task Scaling of Vision-Language-Action Models via Dynamic Grouped Residual Optimization
May 17, 2026Sixu Lin, Yunpeng Qing, Litao Liu +4Vision-Language-Action Models Multi-Task RL
School of Data Science, The Chinese University of Hong Kong (Shenzhen) · Jiangxing Intelligence Technology Inc. · Zhejiang University +3
Don't Let Bandit Feedback Pull Continual LLM-Recommender Updates Off Target
May 17, 2026Taesan Kim, Hyeongjun Yun, Jaegul Choo +1Continual Learning for LLMs Recommender Systems
1SK Telecom · 2KAIST
Reference-Free Reinforcement Learning Fine-Tuning for MT: A Seq2Seq Perspective
May 15, 2026Ernesto Garcia-Estrada, Carlos Escolano, José A. R. FonallosaGroup Relative Policy Optimization Low-Resource MT
Universitat Politècnica de Catalunya Barcelona, Spain
From Failure to Feedback: Group Revision Unlocks Hard Cases in Object-Level Grounding
May 15, 2026Yuyuan Liu, Yiping Ji, Anjie Le +6Vision-Language Grounding Visual Grounding
Department of Engineering Science, University of Oxford · Australian Institute for Machine Learning, Adelaide University · Technical University of Munich +2
Embedding-perturbed Exploration Preference Optimization for Flow Models
May 15, 2026Sujie Hu, Chubin Chen, Jiashu Zhu +3AI Alignment Group Relative Policy Optimization
1Tsinghua University · ⋆Work done during the internship at AMAP · 2AMAP, Alibaba Group
From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery
May 14, 2026Lingzhe Zhang, Tong Jia, Yunpeng Zhai +5RL for Language Models Large Language Model-Guided Optimization
Peking University, Beijing, China · Alibaba Group, China · Nanjing University, Nanjing, China +1
Boosting Reinforcement Learning with Verifiable Rewards via Randomly Selected Few-Shot Guidance
May 14, 2026Kai Yan, Alexander G. Schwing, Yu-Xiong WangRL for Language Model Reasoning Reinforcement Learning with Verifiable Rewards
University of Illinois Urbana-Champaign
Reinforcement Learning with Semantic Rewards Enables Low-Resource Language Expansion without Alignment Tax
May 14, 2026Zeli Su, Ziyin Zhang, Zhou Liu +7Reinforcement Learning Low-Resource MT
Minzu University of China · Ant Group · Shanghai Jiao Tong University +4
What to Ignore, What to React: Visually Robust RL Fine-Tuning of VLA Models
May 13, 2026Yuanfang Peng, Jingjing Fu, Chuheng Zhang +6VLM Robustness Domain Generalization
1Hong Kong University of Science and Technology · 2Microsoft Research Asia · 3Zhejiang University
Discrete Flow Matching for Offline-to-Online Reinforcement Learning
May 12, 2026Fairoz Nower Khan, Nabuat Zaman Nahim, Peizhong JuFlow Matching Reinforcement Learning
Department of Computer Science, University of Kentucky
PriorZero: Bridging Language Priors and World Models for Decision Making
May 12, 2026Junyu Xiong, Yuan Pu, Jia Tang +1LLM-Guided RL Latent World Models
University of Science and Technology of China · Shanghai Artificial Intelligence Laboratory · Nanjing University of Aeronautics and Astronautics +1
TMRL: Diffusion Timestep-Modulated Pretraining Enables Exploration for Efficient Policy Finetuning
May 12, 2026Matthew M. Hong, Jesse Zhang, Anusha Nagabandi +1Robot Policy Adaptation Robot Skill Learning
University of Washington · Amazon FAR
Not How Many, But Which: Parameter Placement in Low-Rank Adaptation
May 12, 2026Arijit Sehanobish, Charles LoveringAdapter Tuning Low-Rank Adaptation
Kensho Technologies
Behavioral Mode Discovery for Fine-tuning Multimodal Generative Policies
May 12, 2026Alberta Longhini, David Emukpere, Jean-Michel Renders +1RL for Robotics Reinforcement Learning
Department of Computer Science, Stanford University, CA, USA
Internalizing Curriculum Judgment for LLM Reinforcement Fine-Tuning
May 11, 2026Han Zheng, Yining Ma, Karthick Gunasekaran +4Reinforcement Learning Meta-Learning
MIT · Amazon AGI
Newton's Lantern: A Reinforcement Learning Framework for Finetuning AC Power Flow Warm Start Models
May 11, 2026Shourya Bose, Helgi Hilmarsson, Dhruv SuriRL Fine-Tuning Power Systems
Pravah
STEPS: Selective On-Policy Self-Distillation for Reasoning
May 11, 2026Jiaxuan Wang, Xuan Ouyang, Zhiyu Chen +2On-Policy Self-Distillation RL for Language Model Reasoning
State Key Laboratory of Novel Software Technology, Nanjing University · School of Intelligence Science and Technology, Nanjing University · AMAP, Alibaba Group +2
Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning
May 10, 2026Meng Lou, Hanzhong Guo, Linwei Chen +1Class-Incremental Learning Domain-Incremental Learning
The University of Hong Kong · The Hong Kong University of Science and Technology · Hong Kong Generative AI Research and Development Center
MARLaaS: Multi-Tenant Asynchronous Reinforcement Learning as a Service
May 8, 2026Timothy Tin Long Yu, Gursimran Singh, Ge Shi +3Asynchronous RL Reinforcement Learning
Huawei Technologies Canada
OracleTSC: Oracle-Informed Reward Hurdle and Uncertainty Regularization for Traffic Signal Control
May 8, 2026Darryl Jacob, Xinyu Liu, Muchao Ye +2Reward Shaping Language Model-Based Control
Department of Computer Science and Software Engineering Auburn University · Department of Computer Science2026 University of Iowa · Department of Electrical and Computer EngineeringMay Clemson University

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EXPO-FT: Sample-Efficient Reinforcement Learning Finetuning for Vision-Language-Action Models

Perry Dong, Kuo-Han Hung, Tian Gao, Dorsa Sadigh+1

Stanford University

Robot Skill Learning Robotic RL Efficient VLA Models Robotic Manipulation Data-Efficient Robot Learning Vision-Language-Action Models+2

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The ability to efficiently and reliably learn new tasks has been a foundational challenge in robotics. Vision-Language-Action (VLA) models have demonstrated strong generalization across diverse manipulation tasks, yet pretrained policies consistently fall short of the reliability required for real-world deployment. Reinforcement learning (RL) fine-tuning offers a promising path to bridge this gap, but existing approaches either train from scratch without fully leveraging pretrained priors, or fine-tune VLAs without achieving the sample efficiency and success rates that practical deployment demands. We present EXPO-FT, a system for stable, sample-efficient RL finetuning of pretrained VLA policies that closes this gap. Our system solves a suite of challenging manipulation tasks, including routing string lights and inserting the plug to light it up, striking a pool ball into a pocket, and inserting a flower into a wine bottle, each requiring combinations of high precision, dynamic actions, and robustness to varied initial states. Our system achieves perfect task performance (30/30 successes) across all evaluated tasks within an average of 19.1 minutes of online robot data, outperforming both prior RL-from-scratch and VLA finetuning approaches. We release an open-source codebase with the aim of facilitating broader adoption of RL finetuning of VLA models in robotics.

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Latest papers 162