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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RefineRL: Advancing Competitive Programming with Self-Refinement Reinforcement Learning
Apr 1, 2026Shaopeng Fu, Xingxing Zhang, Li Dong +1RL for Code Generation LLM Self-Refinement
Work done during an internship at Microsoft Research · KAUST · Microsoft Research
Constrained Group Relative Policy Optimization
Feb 5, 2026Roger Girgis, Rodrigue de Schaetzen, Luke Rowe +3Group Relative Policy Optimization Constrained RL
Mila – Quebec AI Institute Université de Montréal
QUATRO: Query-Adaptive Trust Region Policy Optimization for LLM Fine-tuning
Feb 4, 2026Doyeon Lee, Eunyi Lyou, Hyunsoo Cho +3RL for Language Model Reasoning Policy Optimization
Seoul National University · Department of Artificial Intelligence / Institute for Multiscale Matter and Systems, Ewha Woman University · Geogia Institute of Technology
OpenTinker: Separating Concerns in Agentic Reinforcement Learning
Jan 12, 2026Siqi Zhu, Jiaxuan YouAgentic RL LLM Agent Training
University of Illinois Urbana-Champaign
Reinforcement Fine-Tuning of Flow-Matching Policies for Vision-Language-Action Models
Oct 11, 2025Mingyang Lyu, Yinqian Sun, Erliang Lin +4Flow Matching Robotic Manipulation
Brain-inspired Cognitive AI Lab, Institute of Automation, Chinese Academy of Sciences, Beijing, China. · Beijing Institute of AI Safety and Governance, China. · University of Chinese Academy of Sciences (UCAS), Beijing, China. +3
Enhancing Reasoning for Diffusion LLMs via Distribution Matching Policy Optimization
Oct 9, 2025Yuchen Zhu, Wei Guo, Jaemoo Choi +4RL for Language Model Reasoning Policy Optimization
Georgia Institute of Technology.
Oracle-RLAIF: An Improved Fine-Tuning Framework for Multi-modal Video Models using Reinforcement Learning from Ranking Feedback
Oct 2, 2025Derek Shi, Ruben Glatt, Christine Klymko +5Video-Language Models RL Fine-Tuning
Stanford University, CA, USA · Lawrence Livermore National Laboratory, CA, USA · Microsoft, WA, USA
The Three Regimes of Offline-to-Online Reinforcement Learning
Oct 1, 2025Lu Li, Tianwei Ni, Yihao Sun +1Offline RL RL Fine-Tuning
Mila – Québec AI Institute · Université de Montréal · CIFAR AI Chair
Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning
Sep 29, 2025Xin Qiu, Yulu Gan, Conor F. Hayes +6Fine-Tuning Evolutionary Optimization
Cognizant AI Lab, San Francisco, CA, USA · Massachusetts Institute of Technology, Cambridge, MA, USA · University of California, Los Angeles, Los Angeles, CA, USA +1
Nested-ReFT: Efficient Reinforcement Learning for Large Language Model Fine-Tuning via Off-Policy Rollouts
Aug 13, 2025Maxime Heuillet, Yufei Cui, Boxing Chen +2RL for Language Model Reasoning Efficient Language Model Training
Universit´e Laval (IID), Canada · Mila - Qu´ebec AI Institute, Canada · Huawei Noah’s Ark Lab (Montreal Research Center), Canada +1
Reinforcement Fine-Tuning Naturally Mitigates Forgetting in Continual Post-Training
Jul 7, 2025Song Lai, Haohan Zhao, Rong Feng +9Continual Learning for LLMs LLM Post-Training
Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Hong Kong, China · City University of Hong Kong, Hong Kong, China · School of Artificial Intelligence, Jilin University, Changchun, China +3
Efficient Reinforcement Finetuning via Adaptive Curriculum Learning
Apr 7, 2025Taiwei Shi, Yiyang Wu, Linxin Song +2RL for Language Model Reasoning Mathematical Reasoning
University of Southern California · Carnegie Mellon University · Mohamed Bin Zayed University of Artificial Intelligence

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RefineRL: Advancing Competitive Programming with Self-Refinement Reinforcement Learning

Shaopeng Fu, Xingxing Zhang, Li Dong, Furu Wei

Work done during an internship at Microsoft Research · KAUST · Microsoft Research

RL for Code Generation LLM Self-Refinement RL for Language Model Reasoning Competitive Programming RL Fine-Tuning

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While large language models (LLMs) have demonstrated strong performance on complex reasoning tasks such as competitive programming (CP), existing methods predominantly focus on single-attempt settings, overlooking their capacity for iterative refinement. In this paper, we present RefineRL, a novel approach designed to unleash the self-refinement capabilities of LLMs for CP problem solving. RefineRL introduces two key innovations: (1) Skeptical-Agent, an iterative self-refinement agent equipped with local execution tools to validate generated solutions against public test cases of CP problems. This agent always remains skeptical of its own outputs and thereby enforces rigorous self-refinement even when validation suggests correctness. (2) A reinforcement learning (RL) solution to incentivize LLMs to self-refine with only standard RLVR data (i.e., problems paired with their verifiable answers). Extensive experiments on Qwen3-4B and Qwen3-4B-2507 demonstrate that our method yields substantial gains: after our RL training, these compact 4B models integrated with the Skeptical-Agent not only outperform much larger 32B models but also approach the single-attempt performance of 235B models. Notably, although our models are trained solely on CP coding data, RefineRL also substantially improves their performance on mathematical reasoning benchmarks, suggesting that the learned self-refinement behavior can transfer beyond coding-specific tasks. These findings highlight self-refinement as a promising direction for scaling LLMs' general reasoning ability.

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