RL for VLMs

RL: Reinforcement Learning · VLM: Vision-Language Model

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36 papers in the last four weeks, up 227% on the four weeks before. 0.4% of all new papers.

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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)
GRIP-VLM: Group-Relative Importance Pruning for Efficient Vision-Language Models
May 13, 2026Mingzhe Huang, Weijun Wang, Xin Ding +7Efficient VLM Inference Visual Token Pruning
Institute for AI Industry Research (AIR), Tsinghua University · University of Science and Technology of China · Nanjing University +1
D-VLA: A High-Concurrency Distributed Asynchronous Reinforcement Learning Framework for Vision-Language-Action Models
May 13, 2026Yucheng Guo, Yongjian Guo, Zhong Guan +9Reinforcement Learning Communication-Efficient Distributed Training
JDT AI Infra · Tsinghua University · Tianjin University +2
Reinforcing VLAs in Task-Agnostic World Models
May 12, 2026Yucen Wang, Rui Yu, Fengming Zhang +5World Model Learning World Models
Nanjing University · Interns at Microsoft · University of Illinois at Urbana-Champaign +4
Uni-Synergy: Bridging Understanding and Generation for Personalized Reasoning via Co-operative Reinforcement Learning
May 11, 2026Zijun Shen, Sihan Yang, Ruichuan An +5Unified Multimodal Models Multimodal Reasoning
1Peking University · 3CUHK · 4Zhongguancun Academy
Reflection Anchors for Propagation-Aware Visual Retention in Long-Chain Multimodal Reasoning
May 10, 2026Xuan Gong, Hanbo Huang, Hao Zheng +4Vision-Language Models Vision-Language Grounding
Shanghai Jiao Tong University · Lanzhou University
Perception Without Engagement: Dissecting the Causal Discovery Deficit in LMMs
May 10, 2026Jiafeng Liang, Zhihao Zhu, Zihan Zhang +7Causal Discovery Multimodal Large Language Models
Harbin Institute of Technology · Harvard University · Peking University +2
DeltaRubric: Generative Multimodal Reward Modeling via Joint Planning and Verification
May 10, 2026Rui Liu, Dian Yu, Zhenwen Liang +6Multimodal Large Language Models Multimodal Reward Modeling
1Tencent Hunyuan · University of Maryland, College Park · University of North Carolina, Chapel Hill
SeePhys Pro: Diagnosing Modality Transfer and Blind-Training Effects in Multimodal RLVR for Physics Reasoning
May 10, 2026Kun Xiang, Terry Jingchen Zhang, Zirong Liu +15Cross-Modal Learning Multimodal Grounding
Sun Yat-sen University · ETH Zurich · Huawei Technologies Ltd. +1
Reinforcing Multimodal Reasoning Against Visual Degradation
May 10, 2026Rui Liu, Dian Yu, Haolin Liu +6VLM Robustness Image Corruption Robustness
Tencent Hunyuan · University of Maryland, College Park · University of Virginia +1
BalCapRL: A Balanced Framework for RL-Based MLLM Image Captioning
May 8, 2026Shaokai Ye, Vasileios Saveris, Yihao Qian +3Multi-Objective Reinforcement Learning Image Captioning
Apple
RCoT-Seg: Reinforced Chain-of-Thought for Video Reasoning and Segmentation
May 8, 2026Junwei Wen, Deshui Miao, Guangming Lu +2Video Object Segmentation Video Reasoning
Harbin Institute of Technology, Shenzhen · Pengcheng Laboratory · Pazhou Lab (Huangpu)
Structured Role-Aware Policy Optimization for Multimodal Reasoning
May 8, 2026Bingqing Jiang, Difan ZouToken-Level Credit Assignment Multimodal Reasoning
School of Computing & Data Science, The University of Hong Kong · School of Computing & Data Science and Institute of Data Science, The University of Hong Kong
Pest-Thinker: Learning to Think and Reason like Entomologists via Reinforcement Learning
May 7, 2026Xueheng Li, Yu Wang, Tao Hu +6Agricultural Image Analysis VLM Reasoning
Institute of Intelligent Machines, Hefei Institute of Physical Science, Chinese Academy of Sciences · University of Science and Technology of China · 4Hefei University of Technology +1
VISD: Enhancing Video Reasoning via Structured Self-Distillation
May 7, 2026Hao Lin, Kunyang Lv, Xu Jiang +5Video-Language Models Video Reasoning
1HUST · 2Wuhan University · 3Peking University +1
Reward-Decomposed Reinforcement Learning for Immersive Video Role-Playing
May 6, 2026Miao Wang, Yuling Shi, Yijiang Li +8Grounded Text Generation Large Language Model-Based Role-Play Simulation
1Nanjing University · 2Shanghai Jiao Tong University · University of California, San Diego +3
What You Think is What You See: Driving Exploration in VLM Agents via Visual-Linguistic Curiosity
May 5, 2026Haoxi Li, Qinglin Hou, Jianfei Ma +6Intrinsic Motivation RL Exploration
Department of Computer Science and Engineering, Hong Kong University of Science and Technology (HKUST), Hong Kong, China. · Department of Computer Science, University of Southern California, Los Angeles, USA · School of Computing, National University of Singapore, Singapore. +1
Replacing Parameters with Preferences: Federated Alignment of Heterogeneous Vision-Language Models
May 5, 2026Shule Lu, Yujing Wang, Hainan Zhang +5Reward Modeling Preference Optimization
Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing · Institute of Artificial Intelligence, Beihang University, China · MAIS, Institute of Automation, Chinese Academy of Sciences, China +2
GRPO-TTA: Test-Time Visual Tuning for Vision-Language Models via GRPO-Driven Reinforcement Learning
May 5, 2026Yujun Li, Hongyuan Zhang, Yuan YuanVLM Adaptation Test-Time Adaptation of VLMs
School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an 710072, P.R. China · The University of Hong Kong
Perceptual Flow Network for Visually Grounded Reasoning
May 4, 2026Yangfu Li, Yuning Gong, Hongjian Zhan +8Object Hallucination in VLMs VLM Reasoning
1ECNU · 5Ant Group · 2SCU +3
Chart-FR1: Visual Focus-Driven Fine-Grained Reasoning on Dense Charts
May 3, 2026Hongkun Pan, Yuwei Wu, Wanyi Hong +8Data Visualization Multimodal CoT Reasoning
Zhejiang University · State Key Lab of CAD&CG, Zhejiang University · HiThink Research
MIRL: Mutual Information-Guided Reinforcement Learning for Vision-Language Models
May 2, 2026Yin Zhang, Jiaxuan Zhao, Zonghan Wu +5Vision-Language Models Reinforcement Learning
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
Beyond Perceptual Shortcuts: Causal-Inspired Debiasing Optimization for Generalizable Video Reasoning in Lightweight MLLMs
May 2, 2026Jingze Wu, Quan Zhang, Hongfei Suo +2VLM Robustness Video Reasoning
Sun Yat-sen University, China
Odysseus: Scaling VLMs to 100+ Turn Decision-Making in Games via Reinforcement Learning
May 1, 2026Chengshuai Shi, Wenzhe Li, Xinran Liang +10Sequential Decision Making Proximal Policy Optimization
Princeton Language and Intelligence, Princeton University · Fudan University · Tsinghua University
LaST-R1: Reinforcing Robotic Manipulation via Adaptive Physical Latent Reasoning
Apr 30, 2026Hao Chen, Jiaming Liu, Zhonghao Yan +10RL for Robotics Robot Foundation Models
1The Chinese University of Hong Kong · 2State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University · 3Simplexity Robotics
Beyond SFT-to-RL: Pre-alignment via Black-Box On-Policy Distillation for Multimodal RL
Apr 30, 2026Sudong Wang, Weiquan Huang, Xiaomin Yu +9Multimodal Large Language Models Multimodal Reasoning
Hong Kong University of Science and Technology (Guangzhou) · Nanyang Technological University · Tsinghua University +3
WaferSAGE: Large Language Model-Powered Wafer Defect Analysis via Synthetic Data Generation and Rubric-Guided Reinforcement Learning
Apr 30, 2026Ke Xu, Zhongyuan LianVLM Evaluation Visual Question Answering
Shanghai Huahong Grace Semiconductor Manufacturing Corporation, Shanghai, 201203, China · Dept. of Automation, School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China
Improving Vision-language Models with Perception-centric Process Reward Models
Apr 27, 2026Yingqian Min, Kun Zhou, Yifan Li +6Process Reward Models Object Hallucination in VLMs
Gaoling School of Artificial Intelligence, Renmin University of China. · Bytedance. · University of California, San Diego. +1
See Further, Think Deeper: Advancing VLM's Reasoning Ability with Low-level Visual Cues and Reflection
Apr 27, 2026Zhiheng Wu, Tong Wang, Shuning Wang +2Tool Use in VLMs VLM Reasoning
1Baidu Inc. · †Independent researcher. · 2Zhejiang University +1
Omni-o3: Deep Nested Omnimodal Deduction for Deliberative Audio-Visual Reasoning
Apr 27, 2026Zhicheng Zhang, Wentao Gu, Weicheng Wang +5Audio-Visual Understanding Tree Search
Nankai University · Kuaishou Technology · Pengcheng Laboratory +1

May 13, 2026, cs.CLJ/K move · Enter open · S save

PDCR: Perception-Decomposed Confidence Reward for Vision-Language Reasoning

Hee Suk Yoon, Eunseop Yoon, Ji Woo Hong, SooHwan Eom+5

Korea Advanced Institute of Science and Technology (KAIST) · Microsoft Research Asia (MSRA) · University of Illinois at Urbana-Champaign (UIUC)

VLM Reasoning Reinforcement Learning with Verifiable Rewards Visually Grounded Reasoning RL for VLMs

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Reinforcement Learning with Verifiable Rewards (RLVR) traditionally relies on a sparse, outcome-based signal. Recent work shows that providing a fine-grained, model-intrinsic signal (rewarding the confidence growth in the ground-truth answer) effectively improves language reasoning training by providing step-level guidance without costly external models. While effective for unimodal text, we find that naively applying this global reward to vision-language (V-L) reasoning is a suboptimal strategy, as the task is a heterogeneous mix of sparse visual perception and dense textual reasoning. This global normalization creates mixture-induced signal degradation, where the training signal for visual steps is statistically distorted by the predominant textual steps. We propose Perception-Decomposed Confidence Reward (PDCR), a framework that solves this by aligning the reward structure with the task's heterogeneous nature. PDCR first performs an unsupervised skill decomposition, introducing a model-internal Visual Dependence Score to quantify visual reliance and applying a clustering algorithm to separate perception and reasoning steps. Based on this, PDCR computes a decomposed advantage by normalizing confidence gains within each skill cluster. This intra-cluster normalization provides a stable, correctly-scaled signal for both perception and reasoning. We demonstrate that PDCR outperforms the naive, global-reward formulation and sparse-reward baselines on key V-L reasoning benchmarks.

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