Reinforcement Learning with Verifiable Rewards

Also known as RLVR

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59 papers in the last four weeks, up 119% on the four weeks before. 0.6% of all new papers.

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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
Learning from Failures: Correction-Oriented Policy Optimization with Verifiable Rewards
May 14, 2026Mengjie Ren, Jie Lou, Boxi Cao +6RL for Code Generation LLM Self-Correction
Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences · University of Chinese Academy of Sciences · Xiaohongshu Inc.
Learning to Build the Environment: Self-Evolving Reasoning RL via Verifiable Environment Synthesis
May 14, 2026Yucheng Shi, Zhenwen Liang, Kishan Panaganti +3Reinforcement Learning RL for Language Model Reasoning
Tencent HY LLM
GenCircuit-RL: Reinforcement Learning from Hierarchical Verification for Genetic Circuit Design
May 14, 2026Noah FlynnRL for Code Generation Program Synthesis
University of California, Berkeley, CA, USA.
PDCR: Perception-Decomposed Confidence Reward for Vision-Language Reasoning
May 13, 2026Hee Suk Yoon, Eunseop Yoon, Ji Woo Hong +6VLM Reasoning Reinforcement Learning with Verifiable Rewards
Korea Advanced Institute of Science and Technology (KAIST) · Microsoft Research Asia (MSRA) · University of Illinois at Urbana-Champaign (UIUC)
Revisiting Reinforcement Learning with Verifiable Rewards from a Contrastive Perspective
May 13, 2026Feng Zhang, Xinhong Ma, Ziqiang Dong +5RL for Language Model Reasoning Group Relative Policy Optimization
Beijing Institute of Technology · Qwen Business Unit of Alibaba · The Chinese University of Hong Kong, Shenzhen +1
Multi-Rollout On-Policy Distillation via Peer Successes and Failures
May 12, 2026Weichen Yu, Xiaomin Li, Yizhou Zhao +8RL for Language Model Reasoning Language Model Distillation
1Carnegie Mellon University · 2Microsoft · 3Purdue University
AlphaGRPO: Unlocking Self-Reflective Multimodal Generation in UMMs via Decompositional Verifiable Reward
May 12, 2026Runhui Huang, Jie Wu, Rui Yang +2Unified Multimodal Models Multimodal Generation
The University of Hong Kong · Bytedance Seed
Reward Hacking in Rubric-Based Reinforcement Learning
May 12, 2026Anas Mahmoud, MohammadHossein Rezaei, Zihao Wang +3Reward Hacking Reinforcement Learning
Scale AI
StepCodeReasoner: Aligning Code Reasoning with Stepwise Execution Traces via Reinforcement Learning
May 12, 2026Hao Wang, Rui Li, Lei Sha +1RL for Language Model Reasoning Execution-Guided Code Generation
Beihang University, Beijing, China · Peking University, Beijing, China · Zhongguancun Laboratory, Beijing, China +1
Entropy Polarity in Reinforcement Fine-Tuning: Direction, Asymmetry, and Control
May 12, 2026Jiazheng Zhang, Ziche Fu, Junrui Shen +17RL for Language Model Reasoning Policy Optimization
Fudan NLP Group, Honor Device Co., Ltd · University of Hong Kong, Shanghai Jiao Tong University and Tencent Hunyuan
CuSearch: Curriculum Rollout Sampling via Search Depth for Agentic RAG
May 12, 2026Jianghan Shen, Siqi Luo, Xinyu Cheng +6RL for Language Models Reinforcement Learning
1Nanjing University · 2Shanghai Artificial Intelligence Laboratory · University of Hong Kong +1
Selective Off-Policy Reference Tuning with Plan Guidance
May 12, 2026Duc Anh Le, Tien-Phat Nguyen, Thien Huu Nguyen +2Reinforcement Learning RL for Language Model Reasoning
Independent Author · Hanoi University of Science and Technology, Hanoi, Vietnam · University of Oregon, Eugene, OR, USA +1
Understanding and Preventing Entropy Collapse in RLVR with On-Policy Entropy Flow Optimization
May 12, 2026Huimin Xu, Shuai Zhao, Xiaobao Wu +1Reinforcement Learning RL for Language Model Reasoning
Nanyang Technological University, Singapore · Shanghai Jiao Tong University, China · 3VinUniversity, Vietnam
Breaking $\textit{Winner-Takes-All}$ : Cooperative Policy Optimization Improves Diverse LLM Reasoning
May 12, 2026Haoxuan Chen, Tianming Liang, Wei-Shi Zheng +1Reinforcement Learning RL for Language Model Reasoning
1ISEE Lab, Sun Yat-sen University
fg-expo: Frontier-guided exploration-prioritized policy optimization via adaptive kl and gaussian curriculum
May 12, 2026Mingxiong Lin, Zhangquan Gong, Maowen Tang +6Reinforcement Learning RL for Language Model Reasoning
OPPO AI Center
Rebellious Student: Reversing Teacher Signals for Reasoning Exploration with Self-Distilled RLVR
May 11, 2026Jeonghye Kim, Jiwon Jeon, Dongsheng Li +1RL for Language Model Reasoning Language Model Distillation
Microsoft Research · KAIST
Verifiable Process Rewards for Agentic Reasoning
May 11, 2026Huining Yuan, Zelai Xu, Huaijie Wang +6Process Reward Models Credit Assignment in RL
Tsinghua University
Relative Score Policy Optimization for Diffusion Language Models
May 11, 2026Zichao Yu, Shengze Xu, Bingqing Jiang +2RL for Language Model Reasoning Reinforcement Learning with Verifiable Rewards
University of Science and Technology of China · The Chinese University of Hong Kong · The University of Hong Kong
expo: Exploration-prioritized policy optimization via adaptive kl regulation and gaussian curriculum sampling
May 11, 2026Mingxiong Lin, Zhangquan Gong, Maowen Tang +6Reinforcement Learning KL-Regularized RL
OPPO AI Center
BoostAPR: Boosting Automated Program Repair via Execution-Grounded Reinforcement Learning with Dual Reward Models
May 9, 2026Yuanhao Li, Hongbo Wang, Xiaotang Shang +3Automated Program Repair Process Reward Models
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China · University of Luxembourg
Forge: Quality-Aware Reinforcement Learning for NP-Hard Optimization in LLMs
May 9, 2026Xiaozhe Li, Xinyu Fang, Shengyuan Ding +5RL Benchmarks Reinforcement Learning
Tongji University1 · Shanghai AI Lab2 · Zhejiang University3 +3
How You Begin is How You Reason: Driving Exploration in RLVR via Prefix-Tuned Priors
May 9, 2026Yifan Xu, Junren Chen, Yifan ChenRL for Language Model Reasoning Prompt Tuning
Hong Kong Baptist University · University of Maryland, College Park
Breaking the Impasse: Dual-Scale Evolutionary Policy Training for Social Language Agents
May 9, 2026Minzheng Wang, Run Luo, Yanbo Wang +6Reinforcement Learning Self-Play RL
MAIS, Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences · National University of Singapore +1
The Cancellation Hypothesis in Critic-Free RL: From Outcome Rewards to Token Credits
May 9, 2026Tianhao Cheng, Zeyu Huang, Zihan Qiu +5Reinforcement Learning Token-Level Credit Assignment
1Fudan University · 3The University of Edinburgh · 6Qwen Team, Alibaba Group +4
PYTHALAB-MERA: Validation-Grounded Memory, Retrieval, and Acceptance Control for Frozen-LLM Coding Agents
May 8, 2026Mehmet IscanRL for Code Generation Execution-Guided Code Generation
PythaLab, Yildiz Technical University
DUET: Optimize Token-Budget Allocation for Reinforcement Learning with Verifiable Rewards
May 8, 2026Haoyu Hu, Xuandong Zhao, Xuhai "Orson'' Xu +1Reinforcement Learning Group Relative Policy Optimization
Cornell University · University of California, Berkeley · Columbia University
AIPO: Learning to Reason from Active Interaction
May 8, 2026Junnan Liu, Linhao Luo, Thuy-Trang Vu +1RL for Language Model Reasoning LLM-Guided RL
Department of Data Science and AI, Faculty of Information Technology, Monash University, Australia
Rubric-Grounded RL: Structured Judge Rewards for Generalizable Reasoning
May 8, 2026Manish Bhattarai, Ismael Boureima, Nishath Rajiv Ranasinghe +2Reinforcement Learning RL for Language Model Reasoning
Los Alamos National Laboratory, Los Alamos, NM, USA.
Interactive Critique-Revision Training for Reliable Structured LLM Generation
May 8, 2026Fei Xu Yu, Zuyuan Zhang, Mahdi Imani +2LLM Self-Refinement Group Relative Policy Optimization
The George Washington University · Northeastern University · United States Military Academy

May 14, 2026, cs.LGJ/K move · Enter open · S save

Boosting Reinforcement Learning with Verifiable Rewards via Randomly Selected Few-Shot Guidance

Kai Yan, Alexander G. Schwing, Yu-Xiong Wang

University of Illinois Urbana-Champaign

RL for Language Model Reasoning Reinforcement Learning with Verifiable Rewards RL Fine-Tuning Sample-Efficient RL

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Reinforcement Learning with Verifiable Rewards (RLVR) has achieved great success in developing Large Language Models (LLMs) with chain-of-thought rollouts for many tasks such as math and coding. Nevertheless, RLVR struggles with sample efficiency on difficult problems where correct rollouts are hard to generate. Prior works propose to address this issue via demonstration-guided RLVR, i.e., to conduct Supervised FineTuning (SFT) when RL fails; however, SFT often requires a lot of data, which can be expensive to acquire. In this paper, we propose FEST, a FEw-ShoT demonstration-guided RLVR algorithm. It attains compelling results with only 128 demonstrations randomly selected from an SFT dataset. We find that three components are vital for the success: supervised signal, on-policy signal, and decaying weights on the few-shot SFT dataset to prevent overfitting from multiple-epoch training. On several benchmarks, FEST outperforms baselines with magnitudes less SFT data, even matching their performance with full dataset.

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