Reinforcement Learning with Verifiable Rewards

Also known as RLVR

This field studies how to improve language and multimodal models by rewarding outputs whose correctness or task progress can be checked, for example with unit tests, mathematical answers, formal proofs, or simulated environments. Common approaches use policy-gradient methods such as PPO and GRPO, with outcome rewards supplemented by step-level feedback, rollout exploration, or replay to improve credit assignment and data efficiency. Work spans math and coding benchmarks as well as search, GUI, and other interactive agents. A central tension is that rewards must be scalable yet faithful: imperfect or narrow verifiers can be exploited, and a correct final answer does not by itself establish sound reasoning, visual grounding, or robust generalization.

Latest papers

692 papers137 papers in the last four weeks, up 111% on the four weeks before. 0.8% of all new papers.

Selector-Guided Autonomous Curriculum for One-Shot Reinforcement Learning from Verifiable Rewards
May 3, 2026Rudray Dave, Vedang Dubey, Smit Deoghare +1Reinforcement Learning RL for Language Model Reasoning
Department of Artificial Intelligence Sardar Vallabhbhai National Institute of Technology Surat, India
MIRL: Mutual Information-Guided Reinforcement Learning for Vision-Language Models
May 2, 2026Yin Zhang, Jiaxuan Zhao, Zonghan Wu +5VLM Reasoning Vision-Language Models
School of Mathematics, Tianjin University, Tianjin, China · Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China · Shanghai Advanced Institute of Finance (SAIFS), East China Normal University, Shanghai, China +4
Exploring Pass-Rate Reward in Reinforcement Learning for Code Generation
May 1, 2026Xin-Ye Li, Ren-Biao Liu, Yun-Ji Zhang +3RL for Code Generation Reinforcement Learning
State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing 210023, China · School of Artificial Intelligence, Nanjing University, Nanjing 210023, China
ResRL: Boosting LLM Reasoning via Negative Sample Projection Residual Reinforcement Learning
May 1, 2026Zihan Lin, Xiaohan Wang, Jie Cao +6RL for Language Models Reinforcement Learning
MAIS&NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China · Meituan, Beijing, China · School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing, China.
Uniform-Correct Policy Optimization: Breaking RLVR's Indifference to Diversity
May 1, 2026Anamika Lochab, Bolian Li, Ruqi ZhangRL for Language Model Reasoning Group Relative Policy Optimization
Department of Computer Science Purdue University, West Lafayette
ANCORA: Learning to Question via Manifold-Anchored Self-Play for Verifiable Reasoning
Apr 30, 2026Chengcao YangSelf-Play RL RL for Language Model Reasoning
Wuhan University Wuhan, China
Co-Evolving Policy Distillation
Apr 29, 2026Naibin Gu, Chenxu Yang, Qingyi Si +7Reinforcement Learning with Verifiable Rewards Multi-Teacher Knowledge Distillation
Institute of Information Engineering, CAS, 2School of Cyber Security, UCAS, 3JD.COM
How Fast Should a Model Commit to Supervision? Training Reasoning Models on the Tsallis Loss Continuum
Apr 28, 2026Chu-Cheng Lin, Eugene IeSupervised Fine-Tuning Reinforcement Learning
Google
JURY-RL: Votes Propose, Proofs Dispose for Label-Free RLVR
Apr 28, 2026Xinjie Chen, Biao Fu, Jing Wu +4RL for Language Model Reasoning Mathematical Reasoning
Tongyi Lab, Alibaba Group, Hangzhou, China
Hidden States Know Where Reasoning Diverges: Credit Assignment via Span-Level Wasserstein Distance
Apr 25, 2026Xinzhu Chen, Wei He, Huichuan Fan +7Token-Level Credit Assignment Group Relative Policy Optimization
Beijing University of Posts and Telecommunications · Meituan · Tianjin University +3
Outcome Rewards Do Not Guarantee Verifiable or Causally Important Reasoning
Apr 23, 2026Qinan Yu, Alexa Tartaglini, Peter Hase +2CoT Faithfulness Reinforcement Learning with Verifiable Rewards
1Stanford University · 2Schmidt Sciences
V-tableR1: Process-Supervised Multimodal Table Reasoning with Critic-Guided Policy Optimization
Apr 22, 2026Yubo Jiang, Yitong An, Xin Yang +7Multimodal Reasoning Reinforcement Learning with Verifiable Rewards
School of Astronautics, Beihang University, Beijing 102206, China · Longcat Interaction Team, Meituan, Beijing 100102, China · Tianmushan Laboratory, Beihang University, Hangzhou 311115, China
Learning from the Near Future: Temporal Self-Distillation for RLVR
Apr 22, 2026Chuanyu Qin, Chenxu Yang, Qingyi Si +6Reinforcement Learning with Verifiable Rewards Self-Distillation
Institute of Information Engineering, CAS · School of Cyber Security, UCAS
SSL-R1: Self-Supervised Visual Reinforcement Post-Training for Multimodal Large Language Models
Apr 22, 2026Jiahao Xie, Alessio Tonioni, Nathalie Rauschmayr +2Multimodal Large Language Models Reinforcement Learning with Verifiable Rewards
Max Planck Institute for Informatics, SIC · VIA Research Center · Google
GRPO-VPS: Enhancing Group Relative Policy Optimization with Verifiable Process Supervision for Effective Reasoning
Apr 22, 2026Jingyi Wang, Lei Zhu, Tengjin Weng +8RL for Language Model Reasoning Group Relative Policy Optimization
Tsinghua University · Huawei Noah’s Ark Lab · Shenzhen University
Rethinking Reinforcement Fine-Tuning in LVLM: Convergence, Reward Decomposition, and Generalization
Apr 21, 2026Carter Adams, Rafael Oliveira, Gabriel Almeida +1Tool Use in VLMs Reinforcement Learning
Federal University of Bahia
AeSlides: Incentivizing Aesthetic Layout in LLM-Based Slide Generation via Verifiable Rewards
Apr 21, 2026Yiming Pan, Chengwei Hu, Xuancheng Huang +6LLM Alignment Reinforcement Learning with Verifiable Rewards
Beijing Institute of Technology · Zhipu AI (Z.ai)
Prioritizing the Best: Incentivizing Reliable Multimodal Reasoning by Rewarding Beyond Answer Correctness
Apr 20, 2026Mengzhao Jia, Zhihan Zhang, Meng JiangMultimodal Reasoning Multimodal Reward Modeling
University of Notre Dame
When Can LLMs Learn to Reason with Weak Supervision?
Apr 20, 2026Salman Rahman, Jingyan Shen, Anna Mordvina +3Supervised Fine-Tuning RL for Language Model Reasoning
University of California, Los Angeles · New York University · Google
OGER: A Robust Offline-Guided Exploration Reward for Hybrid Reinforcement Learning
Apr 20, 2026Xinyu Ma, Mingzhou Xu, Xuebo Liu +4Reward Shaping RL for Language Model Reasoning
Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen, China · Hithink RoyalFlush Information Network, Hangzhou, China · Computer and Information Science, University of Macau, Macau, China
Different Paths to Harmful Compliance: Behavioral Side Effects and Mechanistic Divergence Across LLM Jailbreaks
Apr 20, 2026Md Rysul Kabir, Zoran TiganjLanguage Model Safety Evaluation LLM Interpretability
Department of Computer Science Luddy School of Informatics, Computing, and Engineering Indiana University Bloomington
Learning from Less: Measuring the Effectiveness of RLVR in Low Data and Compute Regimes
Apr 20, 2026Justin Bauer, Thomas Walshe, Derek Pham +4RL for Language Model Reasoning Small Language Models
Snorkel AI · University of Wisconsin-Madison · University of Oxford
QuantumQA: Enhancing Scientific Reasoning via Physics-Consistent Dataset and Verification-Aware Reinforcement Learning
Apr 20, 2026Songxin Qu, Tai-Ping Sun, Yun-Jie Wang +8Reward Modeling Scientific QA
Institute of Advanced Technology, University of Science and Technology of China · Institute of Artificial Intelligence, Hefei Comprehensive National Science Center · School of Physics, University of Science and Technology of China +2
HEALing Entropy Collapse: Enhancing Exploration in Few-Shot RLVR via Hybrid-Domain Entropy Dynamics Alignment
Apr 20, 2026Zhanyu Liu, Qingguo Hu, Ante Wang +5RL for Language Model Reasoning Few-Shot Learning
Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University), Ministry of Culture and Tourism, China · Tsinghua University · Xiamen Unisound Intelligence Technology Co., Ltd
Learning to Correct: Calibrated Reinforcement Learning for Multi-Attempt Chain-of-Thought
Apr 20, 2026Muhammed Emrullah Ildiz, Halil Alperen Gozeten, Ege Onur Taga +1CoT Reasoning RL for Language Model Reasoning
University of Michigan, Ann Arbor · Google Research
Beyond Static Snapshots: A Grounded Evaluation Framework for Language Models at the Agentic Frontier
Apr 19, 2026Jazmia HenryReward Hacking RL for Language Models
University of Oxford
TinyJudge: Unverifiable Constraint Alignment via Lightweight Specialist Ensembles
Apr 19, 2026Yirong Zeng, Yufei Liu, Xiao Ding +9Reward Modeling LLM Alignment
Harbin Institute of Technology SCIR Lab · Peking University · Huawei Technologies Co., Ltd
Abstain-R1: Calibrated Abstention and Post-Refusal Clarification via Verifiable RL
Apr 18, 2026Skylar Zhai, Jingcheng Liang, Dongyeop KangLLM Hallucination Mitigation RL for Language Model Reasoning
University of Minnesota
MCPO: Mastery-Consolidated Policy Optimization for Large Reasoning Models
Apr 18, 2026Zhaokang Liao, Yingguo Gao, Yi Yang +2RL for Language Model Reasoning Group Relative Policy Optimization
Ant Group · Zhejiang University
EasyVideoR1: Easier RL for Video Understanding
Apr 18, 2026Chuanyu Qin, Chenxu Yang, Qingyi Si +6Reinforcement Learning Video-Language Models
Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China · School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

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Selector-Guided Autonomous Curriculum for One-Shot Reinforcement Learning from Verifiable Rewards

Rudray Dave, Vedang Dubey, Smit Deoghare, Sudhakar Mishra

Department of Artificial Intelligence Sardar Vallabhbhai National Institute of Technology Surat, India

Reinforcement Learning RL for Language Model Reasoning Curriculum RL Reinforcement Learning with Verifiable Rewards

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Recently, Reinforcement Learning from Verifiable Rewards (RLVR) has been established as a highly effective technique for augmenting the math reasoning skills of Large Language Models (LLMs) based on a single instance. Current state-of-the-art 1-shot RLVR models adopt heuristics for selecting instances, mostly based on historical variance in rewards, which we find to be inherently misleading as a measure of transferability value. In this paper, we propose a Selector-Guided Autonomous Curriculum (SGAC) approach, which employs a learnable selector model on a multi-dimensional feature space consisting of success probability, reward variance, output disagreement (entropy), and semantic difficulty level, instead of the static reward variance heuristic. In our empirical evaluation on pools of candidate problems, we observed that output disagreement, rather than reward variance, is the strongest predictor of reasoning gains in subsequent iterations. Leveraging this finding, we develop an autonomous curriculum algorithm for dynamically siphoning candidate problems from a large pool, ranking them by the learned selector, and running micro-bursts of 1-shot GRPO. Our framework is evaluated using the Hendrycks MATH benchmark, with the Qwen2.5-Math-1.5B model serving as the baseline. Our framework obtains an accuracy of 68.0% on the hold-out dataset, which is better than the accuracy obtained from the state-of-the-art model, 64.0%, as well as the 1-shot RLVR checkpoint proposed by Wang et al., which achieved an accuracy of 66.0%. The results confirm that entropy-based intelligent data curation leads to strict reasoning improvement over static training methods, particularly in severely limited data conditions.