{"total":30,"rows":[{"id":"2503.21620","title":"UI-R1: Enhancing Efficient Action Prediction of GUI Agents by Reinforcement Learning","created":"2025-03-27","cited_by_count":168,"area":"lr"},{"id":"2506.17218","title":"Machine Mental Imagery: Empower Multimodal Reasoning with Latent Visual Tokens","created":"2025-06-20","cited_by_count":124,"area":"lr"},{"id":"2505.15810","title":"GUI-G1: Understanding R1-Zero-Like Training for Visual Grounding in GUI Agents","created":"2025-05-21","cited_by_count":74,"area":"lr"},{"id":"2512.14698","title":"TimeLens: Rethinking Video Temporal Grounding with Multimodal LLMs","created":"2025-12-16","cited_by_count":50,"area":"lr"},{"id":"2505.23590","title":"Jigsaw-R1: A Study of Rule-based Visual Reinforcement Learning with Jigsaw Puzzles","created":"2025-05-29","cited_by_count":36,"area":"lr"},{"id":"2602.02994","title":"Video-OPD: Efficient Post-Training of Multimodal Large Language Models for Temporal Video Grounding via On-Policy Distillation","created":"2026-02-03","cited_by_count":36,"area":"lr"},{"id":"2505.23727","title":"PixelThink: Towards Efficient Chain-of-Pixel Reasoning","created":"2025-05-29","cited_by_count":28,"area":"lr"},{"id":"2509.25848","title":"More Thought, Less Accuracy? On the Dual Nature of Reasoning in Vision-Language Models","created":"2025-09-30","cited_by_count":27,"area":"lr"},{"id":"2508.08039","title":"Audio-Thinker: Guiding Audio Language Model When and How to Think via Reinforcement Learning","created":"2025-08-11","cited_by_count":19,"area":"lr"},{"id":"2506.04088","title":"Multimodal Tabular Reasoning with Privileged Structured Information","created":"2025-06-04","cited_by_count":15,"area":"lr"},{"id":"2505.18536","title":"Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models","created":"2025-05-24","cited_by_count":14,"area":"lr"},{"id":"2505.24105","title":"Training LLMs for EHR-Based Reasoning Tasks via Reinforcement Learning","created":"2025-05-30","cited_by_count":14,"area":"lr"},{"id":"2508.10026","title":"SABER: Switchable and Balanced Training for Efficient LLM Reasoning","created":"2025-08-08","cited_by_count":12,"area":"lr"},{"id":"2602.07605","title":"Fine-R1: Make Multi-modal LLMs Excel in Fine-Grained Visual Recognition by Chain-of-Thought Reasoning","created":"2026-02-07","cited_by_count":11,"area":"lr"},{"id":"2508.12365","title":"TaoSR1: The Thinking Model for E-commerce Relevance Search","created":"2025-08-17","cited_by_count":10,"area":"lr"},{"id":"2507.13362","title":"Enhancing Spatial Reasoning in Vision-Language Models via Chain-of-Thought Prompting and Reinforcement Learning","created":"2025-07-06","cited_by_count":9,"area":"lr"},{"id":"2602.23898","title":"Ref-Adv: Exploring MLLM Visual Reasoning in Referring Expression Tasks","created":"2026-02-27","cited_by_count":9,"area":"lr"},{"id":"2603.22847","title":"Rethinking Token-Level Policy Optimization for Multimodal Chain-of-Thought","created":"2026-03-24","cited_by_count":9,"area":"lr"},{"id":"2601.01483","title":"Unified Generation and Self-Verification for Vision-Language Models via Advantage Decoupled Preference Optimization","created":"2026-01-04","cited_by_count":7,"area":"lr"},{"id":"2602.23777","title":"Reasoning-Driven Multimodal LLM for Domain Generalization","created":"2026-02-27","cited_by_count":7,"area":"lr"},{"id":"2604.14568","title":"Learning Adaptive Reasoning Paths for Efficient Visual Reasoning","created":"2026-04-16","cited_by_count":5,"area":"lr"},{"id":"2511.18305","title":"DiVE-k: Differential Visual Reasoning for Fine-grained Image Recognition","created":"2025-11-23","cited_by_count":2,"area":"lr"},{"id":"2510.10541","title":"Rethinking RL Evaluation: Can Benchmarks Truly Reveal Failures of RL Methods?","created":"2025-10-12","cited_by_count":1,"area":"lr"},{"id":"2602.10019","title":"ADORA: Training Reasoning Models with Dynamic Advantage Estimation on Reinforcement Learning","created":"2026-02-10","cited_by_count":1,"area":"lr"},{"id":"2602.10619","title":"Improving Medical Visual Reinforcement Fine-Tuning via Perception and Reasoning Augmentation","created":"2026-02-11","cited_by_count":1,"area":"lr"},{"id":"2604.00479","title":"All Roads Lead to Rome: Incentivizing Divergent Thinking in Vision-Language Models","created":"2026-04-01","cited_by_count":1,"area":"lr"},{"id":"2603.03197","title":"Specificity-aware reinforcement learning for fine-grained open-world classification","created":"2026-03-03","cited_by_count":0,"area":"lr"},{"id":"2604.00013","title":"C2F-Thinker: Coarse-to-Fine Reasoning with Hint-Guided Reinforcement Learning for Multimodal Sentiment Analysis","created":"2026-03-10","cited_by_count":0,"area":"lr"},{"id":"2604.08232","title":"HiRO-Nav: Hybrid ReasOning Enables Efficient Embodied Navigation","created":"2026-04-09","cited_by_count":0,"area":"lr"},{"id":"2609.31911","title":"Enhancing Visual Reasoning in Chest X-Ray Report Generation Using Reinforcement Learning","created":"2026-09-25","cited_by_count":0,"area":"lr"}],"next_cursor":null}