{"total":42,"rows":[{"id":"2410.24164","title":"$\\pi_0$: A Vision-Language-Action Flow Model for General Robot Control","created":"2024-10-31","cited_by_count":2407,"area":"vla"},{"id":"2405.14093","title":"A Survey on Vision-Language-Action Models for Embodied AI","created":"2024-05-23","cited_by_count":279,"area":"vla"},{"id":"2505.18719","title":"VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning","created":"2025-05-24","cited_by_count":176,"area":"vla"},{"id":"2502.05450","title":"ConRFT: A Reinforced Fine-tuning Method for VLA Models via Consistency Policy","created":"2025-02-08","cited_by_count":154,"area":"vla"},{"id":"2509.09674","title":"SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning","created":"2025-09-11","cited_by_count":151,"area":"vla"},{"id":"2505.17016","title":"Interactive Post-Training for Vision-Language-Action Models","created":"2025-05-22","cited_by_count":135,"area":"vla"},{"id":"2505.19789","title":"What Can RL Bring to VLA Generalization? An Empirical Study","created":"2025-05-26","cited_by_count":108,"area":"vla"},{"id":"2505.11917","title":"OneTwoVLA: A Unified Vision-Language-Action Model with Adaptive Reasoning","created":"2025-05-17","cited_by_count":107,"area":"vla"},{"id":"2511.00091","title":"Self-Improving Vision-Language-Action Models with Data Generation via Residual RL","created":"2025-10-30","cited_by_count":78,"area":"vla"},{"id":"2503.03480","title":"SafeVLA: Towards Safety Alignment of Vision-Language-Action Model via Constrained Learning","created":"2025-03-05","cited_by_count":72,"area":"vla"},{"id":"2508.13073","title":"Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey","created":"2025-08-18","cited_by_count":69,"area":"vla"},{"id":"2506.17811","title":"RoboMonkey: Scaling Test-Time Sampling and Verification for Vision-Language-Action Models","created":"2025-06-21","cited_by_count":61,"area":"vla"},{"id":"2502.13508","title":"VLAS: Vision-Language-Action Model With Speech Instructions For Customized Robot Manipulation","created":"2025-02-19","cited_by_count":51,"area":"vla"},{"id":"2505.15660","title":"Exploring the Limits of Vision-Language-Action Manipulations in Cross-task Generalization","created":"2025-05-21","cited_by_count":33,"area":"vla"},{"id":"2506.03350","title":"Adversarial Attacks on Robotic Vision Language Action Models","created":"2025-06-03","cited_by_count":32,"area":"vla"},{"id":"2508.13446","title":"CAST: Counterfactual Labels Improve Instruction Following in Vision-Language-Action Models","created":"2025-08-19","cited_by_count":29,"area":"vla"},{"id":"2512.13636","title":"MindDrive: A Vision-Language-Action Model for Autonomous Driving via Online Reinforcement Learning","created":"2025-12-15","cited_by_count":25,"area":"vla"},{"id":"2512.05107","title":"STARE-VLA: Progressive Stage-Aware Reinforcement for Fine-Tuning Vision-Language-Action Models","created":"2025-12-04","cited_by_count":24,"area":"vla"},{"id":"2606.14409","title":"Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack","created":"2026-06-12","cited_by_count":22,"area":"vla"},{"id":"2601.03044","title":"SOP: A Scalable Online Post-Training System for Vision-Language-Action Models","created":"2026-01-06","cited_by_count":21,"area":"vla"},{"id":"2505.19767","title":"RFTF: Reinforcement Fine-tuning for Embodied Agents with Temporal Feedback","created":"2025-05-26","cited_by_count":20,"area":"vla"},{"id":"2601.09512","title":"CLARE: Continual Learning for Vision-Language-Action Models via Autonomous Adapter Routing and Expansion","created":"2026-01-14","cited_by_count":17,"area":"vla"},{"id":"2511.17889","title":"MobileVLA-R1: Reinforcing Vision-Language-Action for Mobile Robots","created":"2025-11-22","cited_by_count":15,"area":"vla"},{"id":"2506.07127","title":"Human-assisted Robotic Policy Refinement via Action Preference Optimization","created":"2025-06-08","cited_by_count":13,"area":"vla"},{"id":"2604.23775","title":"Vision-Language-Action Safety: Threats, Challenges, Evaluations, and Mechanisms","created":"2026-04-26","cited_by_count":13,"area":"vla"},{"id":"2505.11214","title":"Unveiling the Potential of Vision-Language-Action Models with Open-Ended Multimodal Instructions","created":"2025-05-16","cited_by_count":12,"area":"vla"},{"id":"2510.09976","title":"Reinforcement Fine-Tuning of Flow-Matching Policies for Vision-Language-Action Models","created":"2025-10-11","cited_by_count":9,"area":"vla"},{"id":"2606.05468","title":"FlowPRO: Reward-Free Reinforced Fine-Tuning of Flow-Matching VLAs via Proximalized Preference Optimization","created":"2026-06-03","cited_by_count":8,"area":"vla"},{"id":"2602.20715","title":"IG-RFT: An Interaction-Guided RL Framework for VLA Models in Long-Horizon Robotic Manipulation","created":"2026-02-24","cited_by_count":5,"area":"vla"},{"id":"2603.18091","title":"Action Draft and Verify: A Self-Verifying Framework for Vision-Language-Action Model","created":"2026-03-18","cited_by_count":4,"area":"vla"},{"id":"2607.02092","title":"Guided Action Flow: Q-Guided Inference for Flow-Matching Vision-Language-Action Policies","created":"2026-07-02","cited_by_count":4,"area":"vla"},{"id":"2509.13774","title":"Dual-Actor Fine-Tuning of VLA Models: A Talk-and-Tweak Human-in-the-Loop Approach","created":"2025-09-17","cited_by_count":3,"area":"vla"},{"id":"2606.10568","title":"VeriSpace: Spatially Grounded Action Verification for Vision-Language-Action Models","created":"2026-06-09","cited_by_count":3,"area":"vla"},{"id":"2603.09542","title":"NS-VLA: Towards Neuro-Symbolic Vision-Language-Action Models","created":"2026-03-10","cited_by_count":2,"area":"vla"},{"id":"2603.12510","title":"Red-Teaming Vision-Language-Action Models via Quality Diversity Prompt Generation for Robust Robot Policies","created":"2026-03-12","cited_by_count":2,"area":"vla"},{"id":"2605.17268","title":"Is VLA Reasoning Faithful? Probing Safety of Chain-of-Causation in Autonomous Driving Models","created":"2026-05-17","cited_by_count":2,"area":"vla"},{"id":"2607.02841","title":"CLEAR: Closed-Loop Reinforcement Learning at Scale for End-to-End Autonomous Driving","created":"2026-07-03","cited_by_count":2,"area":"vla"},{"id":"2604.05614","title":"Grounding Hierarchical Vision-Language-Action Models Through Explicit Language-Action Alignment","created":"2026-04-07","cited_by_count":1,"area":"vla"},{"id":"2605.08879","title":"Preserving Foundational Capabilities in Flow-Matching VLAs through Conservative SFT","created":"2026-05-09","cited_by_count":1,"area":"vla"},{"id":"2608.06729","title":"AtlasVLA: Persistent World-Ego State Modeling for Vision-Language-Action Models","created":"2026-08-07","cited_by_count":1,"area":"vla"},{"id":"2607.19399","title":"Leveraging Offline Supervision for Efficient and Generalizable Reinforcement Learning in Large-Scale Vision-Language-Action Models","created":"2026-07-06","cited_by_count":0,"area":"vla"},{"id":"2609.34599","title":"The Low-Rank Structure of VLA Reinforcement Learning","created":"2026-09-28","cited_by_count":0,"area":"vla"}],"next_cursor":null}