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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SCOPE-RL: Optimizing Reasoning Paths Before and After Success
Jul 13, 2026Xiaojian Liu, Han Xu, Jianqiang Xia +6Reward Shaping RL for Language Model Reasoning
Baidu Inc. · Shandong University
SCALECUA: Scaling Computer Use Agents with Verifiable Task Synthesis and Efficient Online RL
Jul 13, 2026Bowen Lv, Xiao Liu, Yanyu Ren +7Computer-Use Agents Reinforcement Learning with Verifiable Rewards
Tsinghua University
When the Reward Suite Is Leaky: A Preregistered Causal Contrast of Natural Verifier False Positives in RLVR
Jul 13, 2026Chuyifei ZhangAlgorithmic Auditing Reinforcement Learning with Verifiable Rewards
Beijing Jiaotong University
SETA: Scaling Environments for Terminal Agents
Jul 12, 2026Qijia Shen, Zhiqi Huang, Vamsidhar Kamanuru +19Reinforcement Learning Terminal Agents
Imperial College London · University College London · SambaNova +5
Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards
Jul 11, 2026Pengfei Cai, Utkarsh Utkarsh, Alan Edelman +2RL for Code Generation Scientific Code Generation
Massachusetts Institute of Technology
Multimodal Reward Hacking in Reinforcement Learning
Jul 10, 2026Jiayu Yao, Yiwei Wang, Anmeng Zhang +5Multimodal Large Language Models Multimodal Reward Modeling
Institute of Computing Technology, Chinese Academy of Sciences · University of California, Merced · Southeast University
Compete Then Collaborate: Frontier AI Teachers Build a Verifiable Curriculum to Improve a Coding Student Beyond Imitation
Jul 9, 2026Miseong Shawn KimCode Generation Curriculum RL
Genesis Cortex AI Inc.
RLVP: Penalize the Path, Reward the Outcome
Jul 8, 2026Bojie Li, Noah ShiAgentic RL Constrained RL
Pine AI · University of Washington
Beyond Static Evaluation: Building Simulation Environments for Scalable Agentic Reinforcement Learning
Jul 7, 2026Akshay Arora, Ishan Nigam, Ashutosh Aggarwal +6Agentic RL AI Agent Evaluation
Uber AI Solutions San Francisco, CA, USA
Self-Review Reinforcement Learning (SRRL) with Cross-Episode Memory and Policy Distillation
Jul 6, 2026Muhammad Zain Amin, Kibele Sebnem YildirimReinforcement Learning RL for Language Model Reasoning
Department of Software Engineering and IT, ´Ecole de Technologie Sup´erieure, Montreal, Canada · Centre int´egr´e de traumatologie, Hˆopital du Sacr´e-Cœur de Montr´eal, Universit´e de Montr´eal, Montreal, Canada · Desautels Faculty of Management, McGill University, Montreal, Canada.
Weak-to-Strong Generalization via Direct On-Policy Distillation
Jul 6, 2026Shiyuan Feng, Huan-ang Gao, Haohan Chi +7RL for Language Model Reasoning Weak-to-Strong Generalization
1SIA-Lab of Tsinghua AIR and ByteDance Seed · Institute for AI Industry Research (AIR), Tsinghua University · 4Peking University +1
Claim-Level Rubric Rewards for Video Caption Reinforcement Learning
Jul 6, 2026Mingqi Gao, Hongyuan Dong, Yifei Chen +6Reward Modeling Video Captioning
1Tsinghua Shenzhen International Graduate School, Tsinghua University · 3LLM Department, Tencent · University of Chinese Academy of Sciences
Reward Granularity in RLVR: Comparing Process and Outcome Reward Structures for Mathematical Reasoning in Small Language Models
Jul 3, 2026Anagha Radhakrishna Palandye, Rebecca Glick, Osheen KaulProcess Reward Models RL for Language Model Reasoning
New York University
Procedural Memory Distillation: Online Reflection for Self-Improving Language Models
Jul 1, 2026Ye Liu, Srijan Bansal, Bo Pang +6Language Model Distillation Reinforcement Learning with Verifiable Rewards
Salesforce AI Research
World Feedback for Clinical Agents: Diagnosing RL in FHIR Environments
Jul 1, 2026Ananya Mantravadi, Harshit Rajgarhia, Prasanna Desikan +1LLM Agent Evaluation AI Agent Benchmarks
Centific Global Solutions, Inc.
Beyond Next-Token Prediction: An RLVR Proof of Concept for Tool-Use Agents on Atlassian Workflows
Jul 1, 2026Karthikeya Aditya Vissa, Sankalp Mane, Ananya Mantravadi +2Tool-Augmented Language Model Agents LLM Agent Workflow Optimization
Centific
Right in the Right Way: LM Training with Verifiable Rewards and Human Demonstrations
Jul 1, 2026Mehul Damani, Isha Puri, Idan Shenfeld +1Reward Hacking RL for Language Models
MIT EECS
Verifiable Rewards for Calibrated Probabilistic Forecasting
Jun 30, 2026Sadanand Singh, Allam Reddy, Manan ChopraProbability Calibration RL for Language Model Reasoning
Cascade Research
Geometry-Preserving Orthonormal Initialization for Low-Rank Adaptation in RLVR
Jun 30, 2026Ruijia Zhang, Jiacheng Zhu, Hanqing Zhu +1Low-Rank Adaptation Reinforcement Learning with Verifiable Rewards
Department of Applied Mathematics and Statistics, Johns Hopkins University, MD, USA. · Meta Superintelligence Labs. · Department of Electrical and Computer Engineering, The University of Texas at Austin, TX, USA. +1
Which Tokens Matter? Adaptive Token Selection for RLVR with the Relative Surprisal Index
Jun 30, 2026Outongyi Lv, Yanzhao Zheng, Yuanwei Zhang +5Reinforcement Learning RL for Language Model Reasoning
1ModelScope Team, 2Alibaba Group, Hangzhou, China. · 3Shanghai Jiao Tong University, Shanghai, China.
Plan Right, Then Plan Tight: Symbolic RL for Efficient Embodied Reasoning
Jun 30, 2026Xiangli Shi, Xiaomeng Zhu, Ye Tian +5Large Language Model-Based Robot Planning Symbolic Planning
1Tsinghua University · 2The Hong Kong University of Science and Technology · 3Tencent +1
Experience Augmented Policy Optimization for LLM Reasoning
Jun 29, 2026Jinda Lu, Kexin Huang, Junkang Wu +7Reinforcement Learning RL for Language Model Reasoning
University of Science and Technology of China · 2Peking University · 3Dartmouth College +1
LatentRevise: Learning from Zero-Hit Reasoning
Jun 29, 2026Yiqiu Guo, Xueting Han, Qi Jia +2RL for Language Model Reasoning Mathematical Reasoning
1Fudan University · 2Microsoft Research Asia · 3Shanghai AI Laboratory +1
Consistency as Inductive Bias: Learning Cross-View Invariance for Robust Multimodal Reasoning
Jun 29, 2026Xin Zou, Haolin Deng, Yibo Yan +6Multi-View Consistency Multimodal Robustness
HKUST (GZ) · HKUST · UIC +1
BV-Blend: Uncertainty-Weighted Historical Baselines for Stable Critic-Free RL with Verifiable Rewards
Jun 27, 2026Yupeng Chang, Yuan Wu, Yi ChangReinforcement Learning RL for Language Model Reasoning
School of Artificial Intelligence, Jilin University · 2Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, MOE, China · 3International Center of Future Science, Jilin University
Tandem Reinforcement Learning with Verifiable Rewards
Jun 26, 2026Difan Jiao, Raghav Singhal, Robert West +1Reinforcement Learning RL for Language Model Reasoning
University of Toronto · EPFL
PerturbCellRL: Verifier-Guided Reinforcement Learning for Single-Cell Perturbation Prediction
Jun 26, 2026Dongxia Wu, Mingyu Li, Yuhui Zhang +4Single-Cell Perturbation Modeling Reinforcement Learning with Verifiable Rewards
Peking University Beijing, China · Stanford University Stanford, CA
Reinforcement Learning without Ground-Truth Solutions can Improve LLMs
Jun 25, 2026Yingyu Lin, Qiyue Gao, Nikki Lijing Kuang +6RL for Code Generation Reinforcement Learning
University of California, San Diego · Snowflake AI Research
RolloutPipe: Overlapping Pipelined Rollout and Training in Disaggregated On-Policy LLM Reinforcement Learning
Jun 25, 2026Rongjian Chen, Jianmin Hu, Kejiang Ye +1Reinforcement Learning RL for Language Model Reasoning
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China · University of Chinese Academy of Sciences, Beijing, China · Southern University of Science and Technology, Shenzhen, China
The Verification Horizon: No Silver Bullet for Coding Agent Rewards
Jun 24, 2026Binghai Wang, Chenlong Zhang, Dayiheng Liu +10Reward Modeling Reward Hacking
Qwen Team

Jul 13, 2026, cs.LGJ/K move · Enter open · S save

SCOPE-RL: Optimizing Reasoning Paths Before and After Success

Xiaojian Liu, Han Xu, Jianqiang Xia, Zhixuan Li+5

Baidu Inc. · Shandong University

Reward Shaping RL for Language Model Reasoning Efficient Language Model Reasoning Reinforcement Learning with Verifiable Rewards Reward Design for RL

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Reinforcement learning with verifiable rewards (RLVR) optimizes LLMs using sparse verifiable final-answer rewards. This sparse anchor reliably verifies whether a trajectory succeeds but provides no direct feedback on the reasoning path that produced it. Before success, prerequisite progress on hard problems receives no reward signal; after success, outcome rewards cannot distinguish well-organized correct trajectories from redundant or locally flawed ones. We introduce SCOPE-RL (Scaffolded Chain Optimization with Process Efficiency), a two-stage framework that densifies this anchor while retaining the GRPO update: Adaptive Scaffolded RL adds prefix-decomposed verifiable rewards on answer-hidden sub-question chains before success, and Quality-Aware Process RL applies correctness-gated process-shape rewards to refine correct trajectories after success. An expert-validated Step-Quality Evaluation Protocol evaluates useful-step density, error localization, and token efficiency beyond final-answer accuracy. On Qwen3-8B-Instruct trained on DAPO-Math and Big-Math, SCOPE-RL improves average accuracy by up to 11.2 pp and reduces reasoning tokens by up to 27.1% over outcome-only GRPO; the gains hold under GSPO and on Qwen3-0.6B-Instruct, indicating that reward-signal densification is complementary to policy-update-level RLVR advances. Code and data are available at https://github.com/tokencraft-lab/SCOPE-RL.

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