RL for Language Model Reasoning

RL: Reinforcement Learning

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MiniOpt: Reasoning to Model and Solve General Optimization Problems with Limited Resources
Jun 24, 2026Ke Zhao, Zixiang Di, Hong Qian +9Large Language Model-Guided Optimization RL for Language Model Reasoning
East China Normal University, Shanghai 200062, China · AntGroup, Hangzhou 310000, China · Southern University of Science and Technology, Shenzhen 518055, China +1
OPERA: Aligning Open-Ended Reasoning via Objective Perplexity-based Reinforcement Learning
Jun 24, 2026Wenxuan Jiang, Zining Fan, Zijian Zhang +6Open-Ended Generation Intrinsic Reward Methods
The Hong Kong Polytechnic University · Peking University · Meituan Longcat Team +2
Learning with a Single Rollout via Monte Carlo Pass@k Critic
Jun 24, 2026Fengdi Che, Yang Liu, Lei Yu +4RL for Language Model Reasoning Credit Assignment in RL
University of Alberta · BIGAI · University of Toronto +4
Efficient and Trainable Language Model Test-Time Scaling via Local Branch Routing
Jun 24, 2026Yutong Yin, Mingyu Jin, Jin Pan +12RL for Language Model Reasoning Test-Time Scaling
1Northwestern University · 2Rutgers University · University of Wisconsin–Madison +7
Transferability for General Reasoning: An Automated Curriculum for Multi-Domain RLVR
Jun 23, 2026Yongjin Yang, Jiarui Liu, Yinghui He +3RL for Language Model Reasoning Curriculum RL
Jinesis Lab, University of Toronto & Vector Institute1 · Carnegie Mellon University2 · Princeton University3 +4
ExTra: Exploratory Trajectory Optimization for Language Model Reinforcement Learning
Jun 23, 2026Wenyang Hu, Junxiang Jia, Zhen Shu +3RL for Language Models RL for Language Model Reasoning
1National University of Singapore · 2SAP
Beyond Trajectory Imitation: Strategy-Guided Policy Optimization for LLM Reasoning
Jun 23, 2026Tianyuan Shi, Canbin Huang, Bei Li +4RL for Language Model Reasoning Language Model Distillation
School of Computer Science and Engineering, Sun Yat-sen University, China · Meituan, Inc., China · Shenzhen Loop Area Institute, China +1
SPIRAL: Learning to Search and Aggregate
Jun 22, 2026Jubayer Ibn Hamid, Ifdita Hasan Orney, Michael Y. Li +5Reinforcement Learning RL for Language Model Reasoning
Stanford University
Provable Benefits of RLVR over SFT for Reasoning Models: Learning to Backtrack Efficiently
Jun 22, 2026Stanley Wei, Juno KimRL for Language Model Reasoning Efficient Language Model Reasoning
1Princeton University · University of California, Berkeley.
Beyond Penalizing Mistakes: Stabilizing Efficiency Training in Large Reasoning Models via Adaptive Correct-Only Rewards
Jun 21, 2026Jungseob Lee, Seungyoon Lee, Seongtae Hong +3Reward Shaping RL for Language Model Reasoning
Korea University · Soongsil University
What are Key Factors for Updates in RL for LLM Reasoning?
Jun 21, 2026Peidong Wang, Demi Wang, Xufang Luo +5Reinforcement Learning RL for Language Model Reasoning
School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China · *Work done during internship at Microsoft Research Asia. · 2Microsoft Research +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
Learning at the Right Pace: Adaptive Data Scheduling Improves LLM Reinforcement Learning
Jun 21, 2026Zicheng Xu, Ruixuan Zhang, Yu-Neng Chuang +7Reinforcement Learning RL for Language Model Reasoning
Johns Hopkins University · Rice University · University of Haifa +2
Fine-Tuning Large Language Models for Quantum Reasoning
Jun 20, 2026Katherine Ip, Casey R. Myers, Udaya Parampalli +2LLM Fine-Tuning RL for Language Model Reasoning
School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia · School of Physics, Mathematics and Computing, The University of Western Australia, 35 Stirling Hwy, Crawley WA, 6009, Australia · Pawsey Supercomputing Centre, 1 Bryce Avenue, Kensington WA, 6151, Australia +1
Mat-Pref: Verifiable-Reward Training Improves Compositional Reasoning in Inorganic Materials
Jun 20, 2026Sarrah R. Mikhail Leung, Taehan Kim, Jeongbin ParkCompositional Reasoning RL for Language Model Reasoning
University of California, Berkeley · 2Machine Learning at Berkeley · University of Michigan
CogniRoute: Learning to Route Social Evidence in Omni-Modal Models
Jun 18, 2026Yifan Shen, Pei Tian, Xinzhuo Li +8RL for Language Model Reasoning Mixture-of-Experts Inference
University of Illinois Urbana-Champaign · 2Columbia University · 3Google
VIMPO: Value-Implicit Policy Optimization for LLMs
Jun 18, 2026Zhewei Kang, Aosong Feng, Sergey Levine +2RL for Language Model Reasoning Policy Optimization
UC Berkeley · Yale University
ADaPT: Token-Level Decoupling for Efficient Large Reasoning Models
Jun 18, 2026Tingyun Li, Zishang Jiang, Jinyi Han +8Inference-Time Optimization RL for Language Model Reasoning
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
Beyond Entropy: Learning from Token-Level Distributional Deviations for LLM Reasoning
Jun 18, 2026Xuanzhi Feng, Zhengyang Li, Zeyu Liu +6Reinforcement Learning RL for Language Model Reasoning
The Hong Kong University of Science and Technology, Hong Kong, China · Sichuan University, Chengdu, China · The Hong Kong Polytechnic University, Hong Kong, China
Manifold Bandits: Bayesian Curriculum Learning over the Latent Geometry of Large Language Models
Jun 18, 2026Darrien McKenzie, Nicklas Hansen, Xiaolong WangMulti-Armed Bandits RL for Language Model Reasoning
University of California, San Diego
Rethinking Reward Supervision: Rubric-Conditioned Self-Distillation
Jun 17, 2026Siyi Gu, Jialin Chen, Sophia Zhou +2RL for Language Model Reasoning Language Model Distillation
Yale University
STARE: Surprisal-Guided Token-Level Advantage Reweighting for Policy Entropy Stability
Jun 17, 2026Haipeng Luo, Qingfeng Sun, Songli Wu +4RL for Language Model Reasoning Maximum Entropy RL
Shenzhen International Graduate School, Tsinghua University · Tencent Hunyuan
ThinkDeception: A Progressive Reinforcement Learning Framework for Interpretable Multimodal Deception Detection
Jun 17, 2026Jinhao Song, Shan Liang, Yiqun Yue +2RL for Language Model Reasoning Multimodal Large Language Models
Xi’an Jiaotong-Liverpool University · Suzhou, China
GraphPO: Graph-based Policy Optimization for Reasoning Models
Jun 17, 2026Yuliang Zhan, Xinyu Tang, Jian Li +7Reinforcement Learning LLM Reasoning with Graphs
Gaoling School of Artificial Intelligence, Renmin University of China · Ant Group
REVES: REvision and VErification--Augmented Training for Test-Time Scaling
Jun 17, 2026Yuanxin Liu, Ruida Zhou, Xinyan Zhao +6LLM Self-Correction LLM Answer Verification
Northwestern University · Amazon AGI · Qualcomm AI Research +1
Learning from Your Own Mistakes: Constructing Learnable Micro-Reflective Trajectories for Self-Distillation
Jun 17, 2026Zhilin Huang, Hang Gao, Ziqiang Dong +6LLM Self-Correction CoT Distillation
Qwen Business Unit of Alibaba · Tsinghua University · Peking University
Beyond Reward Engineering: A Data Recipe for Long-Context Reinforcement Learning
Jun 17, 2026Xiaoyue Xu, Sikui Zhang, Xiaorong Wang +2Training Data Curation RL for Language Model Reasoning
OpenBMB · Tsinghua University
Learning from Own Solutions: Self-Conditioned Credit Assignment for Reinforcement Learning with Verifiable Rewards
Jun 17, 2026Yingyu Shan, Yuhang Guo, Zihao Cheng +7Token-Level Credit Assignment RL for Language Model Reasoning
Beijing Institute of Technology · Beihang University · Independent Researcher
From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoning
Jun 16, 2026Lingjing Kong, Xin Liu, Guangyi Chen +9LLM Fine-Tuning RL for Language Model Reasoning
Carnegie Mellon University · Mohamed bin Zayed University of Artificial Intelligence · Institute of Foundation Models +1
Dynamic Rollout Editing for Reducing Overthinking in RL-Trained Reasoning Models
Jun 16, 2026Zihao Wei, Wenjie Shi, Liang Pang +8Overthinking in Language Models RL for Language Model Reasoning
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China

Jun 24, 2026, cs.LGJ/K move · Enter open · S save

MiniOpt: Reasoning to Model and Solve General Optimization Problems with Limited Resources

Ke Zhao, Zixiang Di, Hong Qian, Xiang Shu+8

East China Normal University, Shanghai 200062, China · AntGroup, Hangzhou 310000, China · Southern University of Science and Technology, Shenzhen 518055, China+1

Large Language Model-Guided Optimization RL for Language Model Reasoning Policy Optimization Natural Language to Optimization Modeling Reward Design for RL

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Achieving strong optimization generalization across diverse optimization problems while requiring limited training resources remains a challenging problem for optimization-oriented large language models (LLMs). Existing approaches typically rely on large-scale supervised datasets, costly reasoning annotations, and expensive intermediate step verification, resulting in substantial training overhead. To address these challenges, we propose MiniOpt, a reinforcement learning framework that learns to solve optimization problems through an "reasoning-to-model-and-solve" paradigm. MiniOpt decomposes optimization reasoning into structured optimization modeling and executable solver generation. Building upon this paradigm, we introduce OptReward, a reward function with hierarchical score structure that jointly evaluates formulation and solution, enabling effective policy learning without expert demonstrations. We further develop an optimization-oriented policy optimization strategy that improves exploration efficiency and stabilizes reinforcement learning for compact models. Extensive experiments show that MiniOpt-3B exhibits strong optimization generalization across various optimization types, problem scenarios, and task domains. For models with fewer than 10B parameters, MiniOpt series achieves the highest average solving accuracy (SA). For models with more than 10B parameters, MiniOpt still shows competitive performance. These results suggest that optimization-oriented reward design and reinforcement learning provide an effective pathway for developing compact optimization-specialized language models with strong optimization generalization capabilities. The code is available at https://github.com/Hsiang-1/MiniOpt.

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