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.

Incentivizing Parametric Knowledge via Reinforcement Learning with Verifiable Rewards for Cross-Cultural Entity Translation
Apr 18, 2026Jiang Zhou, Xiaohu Zhao, Xinwei Wu +8RL for Language Models Factual Knowledge in Language Models
TJUNLP Lab, Tianjin University, China · Alibaba Group, China
Detecting and Suppressing Reward Hacking with Gradient Fingerprints
Apr 17, 2026Songtao Wang, Quang Hieu Pham, Fangcong Yin +4Reward Hacking Reinforcement Learning with Verifiable Rewards
AUniversity of Alberta · N New York University · LLMU Munich +1
LLMs Gaming Verifiers: RLVR can Lead to Reward Hacking
Apr 16, 2026Lukas Helff, Quentin Delfosse, David Steinmann +6Reward Hacking Logical Reasoning
TU Darmstadt · DFKI · Intrinsic +4
DiPO: Disentangled Perplexity Policy Optimization for Fine-grained Exploration-Exploitation Trade-Off
Apr 15, 2026Xiaofan Li, Ming Yang, Zhiyuan Ma +9Reinforcement Learning RL for Language Model Reasoning
East China Normal University · Ant Group · Xiamen University +1
Balanced Aggregation: Understanding and Fixing Aggregation Bias in GRPO
Apr 14, 2026Zhiyuan Zeng, Jiameng Huang, Zhangyue Yin +8Reinforcement Learning RL for Language Model Reasoning
Fudan University · M-A-P · Peking University +3
PubSwap: Public-Data Off-Policy Coordination for Federated RLVR
Apr 14, 2026Anupam Nayak, Baris Askin, Muhammed Ustaomeroglu +2Federated RL Reinforcement Learning with Verifiable Rewards
Carnegie Mellon University
Self-Distillation Zero: Self-Revision Turns Binary Rewards into Dense Supervision
Apr 13, 2026Yinghui He, Simran Kaur, Adithya Bhaskar +7Language Model Self-Improvement Self-Supervised Knowledge Distillation
Princeton University · University of Toronto · Carnegie Mellon University
Low-rank Optimization Trajectories Modeling for LLM RLVR Acceleration
Apr 13, 2026Zhipeng Chen, Tao Qian, Wayne Xin Zhao +1RL Post-Training Reinforcement Learning with Verifiable Rewards
Gaoling School of Artificial Intelligence, Renmin University of China. · China University of Mining and Technology (Beijing).
Where Hindsight Credit Can Reside: A Signed-Capacity View of Token Updates in RLVR
Apr 13, 2026Yuhang He, Haodong Wu, Siyi Liu +7RL for Language Model Reasoning Token-Level Credit Assignment
The Hong Kong University of Science and Technology (Guangzhou) · Huawei Technologies Ltd.
Adapt to Thrive! Adaptive Power-Mean Policy Optimization for Improved LLM Reasoning
Apr 11, 2026Yiming Huang, Zhenbo Shi, Shuzheng Gao +3RL for Language Model Reasoning Policy Optimization
Harbin Institute of Technology, Shenzhen · The Chinese University of Hong Kong · Peng Cheng Laboratory
Instructing LLMs to Negotiate using Reinforcement Learning with Verifiable Rewards
Apr 10, 2026Shuze Daniel Liu, Claire Chen, Jiabao Sean Xiao +4Automated Negotiation Multi-Agent Negotiation
Purdue University · California Institute of Technology · University of Illinois Urbana-Champaign +1
Controllable and Verifiable Tool-Use Data Synthesis for Agentic Reinforcement Learning
Apr 10, 2026Siyuan Xu, Shiyang Li, Xin Liu +9Tool Use RL for Tool Use
The Pennsylvania State University · Amazon · Georgia Institute of Technology
Visually-Guided Policy Optimization for Multimodal Reasoning
Apr 10, 2026Zengbin Wang, Feng Xiong, Liang Lin +5VLM Reasoning RL for VLMs
AMAP, Alibaba Group · SYSU · BUPT
Backdoors in RLVR: Jailbreak Backdoors in LLMs From Verifiable Reward
Apr 10, 2026Weiyang Guo, Zesheng Shi, Zeen Zhu +3LLM Backdoor Attacks LLM Jailbreak Attacks
Harbin Institute of Technology, Shenzhen, China · Huawei Technologies Co., Ltd.
PerMix-RLVR: Preserving Persona Expressivity under Verifiable-Reward Alignment
Apr 10, 2026Jihwan Oh, Soowon Oh, Murad Aghazada +3Personalized Language Model Alignment Language Model Robustness
KAIST AI · Samsung Advanced Institute of Technology
Skip-Connected Policy Optimization for Implicit Advantage
Apr 9, 2026Fengwei Teng, Jinyi Bai, Xinhao Yao +3RL for Language Model Reasoning Reinforcement Learning with Verifiable Rewards
Mohamed bin Zayed University of Artificial Intelligence · Renmin University of China · Carnegie Mellon University +1
SUPERNOVA: Eliciting General Reasoning in LLMs with Reinforcement Learning on Natural Instructions
Apr 9, 2026Ashima Suvarna, Kendrick Phan, Mehrab Beikzadeh +2RL for Language Model Reasoning LLM Reasoning
University of California, Los Angeles
Mitigating Distribution Sharpening in Math RLVR via Distribution-Aligned Hint Synthesis and Backward Hint Annealing
Apr 9, 2026Pei-Xi Xie, Che-Yu Lin, Cheng-Lin YangReinforcement Learning LLM Mathematical Reasoning
CyCraft AI Lab, Taiwan
An Imperfect Verifier is Good Enough: Learning with Noisy Rewards
Apr 9, 2026Andreas Plesner, Francisco Guzmán, Anish AthalyeRL for Code Generation Reinforcement Learning
Handshake AI · ETH Zurich
OmniDiagram: Advancing Unified Diagram Code Generation via Visual Interrogation Reward
Apr 7, 2026Haoyue Yang, Xuanle Zhao, Xuexin Liu +2Code Generation RL for Code Generation
Institute of Automation, Chinese Academy of Sciences · University of Chinese Academy of Sciences · Zhejiang University
Don't Act Blindly: Robust GUI Automation via Action-Effect Verification and Self-Correction
Apr 7, 2026Yuzhe Zhang, Xianwei Xue, Xingyong Wu +8GUI Agents GUI Automation
Beijing University of Technology, Beijing, China · Baidu Inc., Beijing, China
Asymmetric Advantage Modulation Calibrates Entropy Dynamics in RLVR
Apr 6, 2026Hengrui Gu, Xiaotian Han, Yujing Bian +2RL for Language Model Reasoning Reinforcement Learning with Verifiable Rewards
North Carolina State University · Case Western Reserve University · Oak Ridge National Laboratory
Cog-DRIFT: Exploration on Adaptively Reformulated Instances Enables Learning from Hard Reasoning Problems
Apr 6, 2026Justin Chih-Yao Chen, Archiki Prasad, Zaid Khan +4RL for Language Model Reasoning LLM Reasoning
UNC Chapel Hill
Delay, Plateau, or Collapse: Evaluating the Impact of Systematic Verification Error on RLVR
Apr 6, 2026Kazuki Egashira, Mark Vero, Jasper Dekoninck +3Reinforcement Learning with Verifiable Rewards RL for Language Model Reasoning
ETH Zurich · Max Planck Institute for Intelligent Systems, Tübingen
Can LLMs Learn to Reason Robustly under Noisy Supervision?
Apr 5, 2026Shenzhi Yang, Guangcheng Zhu, Bowen Song +7LLM Reasoning Noisy-Label Learning
Zhejiang University · Ant Group · University of Wisconsin-Madison
OASES: Outcome-Aligned Search-Evaluation Co-Training for Agentic Search
Apr 4, 2026Erhan Zhang, Yiqun Chen, Zechun Niu +6Agentic Search Process Reward Models
Renmin University of China · University of Southern California · Xiaohongshu Inc.
Self-Distilled RLVR
Apr 3, 2026Chenxu Yang, Chuanyu Qin, Qingyi Si +7On-Policy Self-Distillation Reinforcement Learning with Verifiable Rewards
Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China · School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China
Correct Answers from Sound Reasoning: Verifiable Process Supervision for Language Models
Apr 3, 2026Kyuyoung Kim, Kevin Wang, Yunfei Xie +7Process Supervision RL for Language Model Reasoning
KAIST AI · University of Texas, Austin · Rice University +3
Reinforcement Learning-based Semi-supervised Knowledge Distillation with LLM-as-a-Judge
Apr 3, 2026Yiyang Shen, Lifu Tu, Weiran WangRL for Language Model Reasoning Knowledge Distillation
University of Iowa · Oracle AI
Unifying Group-Relative and Self-Distillation Policy Optimization via Sample Routing
Apr 2, 2026Gengsheng Li, Tianyu Yang, Junfeng Fang +6RL for Language Model Reasoning On-Policy Self-Distillation
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences · National University of Singapore +2

Apr 18, 2026J/K move · Enter open · S save

Incentivizing Parametric Knowledge via Reinforcement Learning with Verifiable Rewards for Cross-Cultural Entity Translation

Jiang Zhou, Xiaohu Zhao, Xinwei Wu, Tianyu Dong+7

TJUNLP Lab, Tianjin University, China · Alibaba Group, China

RL for Language Models Factual Knowledge in Language Models Reinforcement Learning with Verifiable Rewards Machine Translation

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Cross-cultural entity translation remains challenging for large language models (LLMs) as literal or phonetic renderings are usually yielded instead of culturally appropriate translations in context. However, relevant knowledge may already be encoded in model parameters during large-scale pre-training. To incentivize the effective use of parametric knowledge, we propose EA-RLVR (Entity-Anchored Reinforcement Learning with Verifiable Rewards), a training framework that optimizes cross-cultural entity translation without relying on external knowledge bases. EA-RLVR anchors supervision on a verifiable, entity-level reward signal and incorporates lightweight structural gates to stabilize optimization. This design steers the model toward learning a robust reasoning process rather than merely imitating reference translations. We evaluate EA-RLVR on XC-Translate and observe consistent improvements in both entity translation accuracy and out-of-domain generalization. Specifically, training on merely 7k samples boosts Qwen3-14B's entity translation accuracy from 23.66% to 31.87% on a 50k test set comprising entirely unseen entities. The learned entity translation ability also transfers to general translation, yielding +1.35 XCOMET on WMT24++, which scales to +1.59 with extended optimization. Extensive analyses of

pass@k

dynamics and reward formulations attribute these gains to superior sampling efficiency and a stable optimization landscape.