{"total":29,"rows":[{"id":"2605.30280","title":"Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments","created":"2026-05-28","cited_by_count":44,"area":"vla"},{"id":"2608.11739","title":"G0.5: One Autoregressive Stream for Robot Reasoning and Action","created":"2026-08-12","cited_by_count":18,"area":"vla"},{"id":"2511.14148","title":"AsyncVLA: Asynchronous Flow Matching for Vision-Language-Action Models","created":"2025-11-18","cited_by_count":17,"area":"vla"},{"id":"2603.11653","title":"Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning","created":"2026-03-12","cited_by_count":17,"area":"vla"},{"id":"2510.25889","title":"$\\pi_\\texttt{RL}$: Online RL Fine-tuning for Flow-based Vision-Language-Action Models","created":"2025-10-29","cited_by_count":16,"area":"vla"},{"id":"2603.27670","title":"ProgressVLA: Progress-Guided Diffusion Policy for Vision-Language Robotic Manipulation","created":"2026-03-29","cited_by_count":10,"area":"vla"},{"id":"2602.12628","title":"Beyond Imitation: Reinforcement Learning-Based Sim-Real Co-Training for VLA Models","created":"2026-02-13","cited_by_count":9,"area":"vla"},{"id":"2603.02083","title":"$\\pi$-StepNFT: Wider Space Needs Finer Steps in Online RL for Flow-based VLAs","created":"2026-03-02","cited_by_count":6,"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":"2603.07647","title":"TempoFit: Plug-and-Play Layer-Wise Temporal KV Memory for Long-Horizon Vision-Language-Action Manipulation","created":"2026-03-08","cited_by_count":5,"area":"vla"},{"id":"2607.27782","title":"RedFlow: Redirect Failure into Action-Level Corrections for Flow-matching VLA Policy","created":"2026-07-30","cited_by_count":4,"area":"vla"},{"id":"2606.11743","title":"TacCoRL: Integrating Tactile Feedback into VLA via Simulation","created":"2026-06-10","cited_by_count":3,"area":"vla"},{"id":"2608.03483","title":"Continue or Replan? Bernoulli-Continuation Policy Learning for Adaptive Horizon Execution","created":"2026-08-04","cited_by_count":3,"area":"vla"},{"id":"2606.08653","title":"FiberTune: Preserving Action-Fiber Visual Residuals in Vision-Language-Action Fine-Tuning","created":"2026-06-07","cited_by_count":2,"area":"vla"},{"id":"2606.09630","title":"ReCoVLA: VLM-Guided Reward Compilation for Failure Recovery in Vision-Language-Action Policies","created":"2026-06-08","cited_by_count":2,"area":"vla"},{"id":"2606.22540","title":"PolicyTrim: Boosting Intrinsic Policy Efficiency of Vision-Language-Action Models","created":"2026-06-21","cited_by_count":2,"area":"vla"},{"id":"2605.13105","title":"What to Ignore, What to React: Visually Robust RL Fine-Tuning of VLA Models","created":"2026-05-13","cited_by_count":1,"area":"vla"},{"id":"2606.14375","title":"Elastic Queries Reinforcement Learning: Self-Aware Policy Execution for VLA Models","created":"2026-06-12","cited_by_count":1,"area":"vla"},{"id":"2606.15568","title":"SAPS: Shared Autonomy for Policy Steering by Blending Teleoperation with a Pretrained VLA","created":"2026-06-14","cited_by_count":1,"area":"vla"},{"id":"2607.09042","title":"Learning More from Less: Reinforcement Learning from Hindsight","created":"2026-07-10","cited_by_count":0,"area":"vla"},{"id":"2607.16074","title":"JoyNexus: Service-Oriented Multi-Tenant Post-Training for VLA Models","created":"2026-07-17","cited_by_count":0,"area":"vla"},{"id":"2607.26513","title":"Explicit Kinematic Guidance from Analytic Concepts for Vision-Language-Action Models","created":"2026-07-29","cited_by_count":0,"area":"vla"},{"id":"2609.28838","title":"Uncertainty-Gated Exploration Noise Suppresses Task Collapse in Online RL Fine-Tuning of a Flow-Matching Vision-Language-Action Policy","created":"2026-09-23","cited_by_count":0,"area":"vla"},{"id":"2609.32634","title":"PF-RL: Progress Field Reinforcement Learning via Goal-Conditioned Value Geometry for Vision-Language-Action Models","created":"2026-09-26","cited_by_count":0,"area":"vla"},{"id":"2609.32698","title":"SEES: A Self-Evolving Embodied System via Failure-Guided VLA Policy Adaptation","created":"2026-09-26","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"},{"id":"2610.06843","title":"Recursive Video In-Context Learning for Agentic Robot","created":"2026-10-05","cited_by_count":0,"area":"vla"},{"id":"2610.18874","title":"ChunkAct: Action Chunking for Agentic Robotics","created":"2026-10-13","cited_by_count":0,"area":"vla"},{"id":"2610.19517","title":"VLRLA: Deployment-Time Adaptation of Vision-Language-Action Models through In-Context Reinforcement Learning","created":"2026-10-14","cited_by_count":0,"area":"vla"}],"next_cursor":null}