{"total":95,"rows":[{"id":"2510.13626","title":"LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models","created":"2025-10-15","cited_by_count":303,"area":"vla"},{"id":"2511.14759","title":"$\\pi^{*}_{0.6}$: a VLA That Learns From Experience","created":"2025-11-18","cited_by_count":303,"area":"vla"},{"id":"2510.03827","title":"LIBERO-PRO: Towards Robust and Fair Evaluation of Vision-Language-Action Models Beyond Memorization","created":"2025-10-04","cited_by_count":179,"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":"2507.01925","title":"A Survey on Vision-Language-Action Models: An Action Tokenization Perspective","created":"2025-07-02","cited_by_count":96,"area":"vla"},{"id":"2509.06951","title":"F1: A Vision-Language-Action Model Bridging Understanding and Generation to Actions","created":"2025-09-08","cited_by_count":86,"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":"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":"2512.01801","title":"GR-RL: Going Dexterous and Precise for Long-Horizon Robotic Manipulation","created":"2025-12-01","cited_by_count":45,"area":"vla"},{"id":"2510.24795","title":"A Survey on Efficient Vision-Language-Action Models","created":"2025-10-27","cited_by_count":34,"area":"vla"},{"id":"2601.04061","title":"CLAP: Contrastive Latent Action Pretraining for Learning Vision-Language-Action Models from Human Videos","created":"2026-01-07","cited_by_count":31,"area":"vla"},{"id":"2510.06710","title":"RLinf-VLA: A Unified and Efficient Framework for Reinforcement Learning of Vision-Language-Action Models","created":"2025-10-08","cited_by_count":28,"area":"vla"},{"id":"2511.15605","title":"SRPO: Self-Referential Policy Optimization for Vision-Language-Action Models","created":"2025-11-19","cited_by_count":27,"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":"2506.17639","title":"RLRC: Reinforcement Learning-based Recovery for Compressed Vision-Language-Action Models","created":"2025-06-21","cited_by_count":23,"area":"vla"},{"id":"2512.14666","title":"EVOLVE-VLA: Test-Time Training from Environment Feedback for Vision-Language-Action Models","created":"2025-12-16","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":"2511.16203","title":"When Alignment Fails: Multimodal Adversarial Attacks on Vision-Language-Action Models","created":"2025-11-20","cited_by_count":20,"area":"vla"},{"id":"2511.01331","title":"RobustVLA: Robustness-Aware Reinforcement Post-Training for Vision-Language-Action Models","created":"2025-11-03","cited_by_count":18,"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":"2509.04063","title":"Balancing Signal and Variance: Adaptive Offline RL Post-Training for VLA Flow Models","created":"2025-09-04","cited_by_count":15,"area":"vla"},{"id":"2605.00416","title":"Learning While Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies","created":"2026-05-01","cited_by_count":15,"area":"vla"},{"id":"2601.06748","title":"On-the-Fly VLA Adaptation via Test-Time Reinforcement Learning","created":"2026-01-11","cited_by_count":14,"area":"vla"},{"id":"2509.22643","title":"VLA-Reasoner: Empowering Vision-Language-Action Models with Reasoning via Online Monte Carlo Tree Search","created":"2025-09-26","cited_by_count":13,"area":"vla"},{"id":"2511.21192","title":"When Robots Obey the Patch: Universal Transferable Patch Attacks on Vision-Language-Action Models","created":"2025-11-26","cited_by_count":13,"area":"vla"},{"id":"2512.07472","title":"Affordance Field Intervention: Enabling VLAs to Escape Memory Traps in Robotic Manipulation","created":"2025-12-08","cited_by_count":13,"area":"vla"},{"id":"2602.10503","title":"Towards Long-Lived Robots: Continual Learning VLA Models via Reinforcement Fine-Tuning","created":"2026-02-11","cited_by_count":13,"area":"vla"},{"id":"2603.26666","title":"VLA-OPD: Bridging Offline SFT and Online RL for Vision-Language-Action Models via On-Policy Distillation","created":"2026-03-27","cited_by_count":13,"area":"vla"},{"id":"2511.16166","title":"EvoVLA: Self-Evolving Vision-Language-Action Model","created":"2025-11-20","cited_by_count":11,"area":"vla"},{"id":"2605.25477","title":"EXPO-FT: Sample-Efficient Reinforcement Learning Finetuning for Vision-Language-Action Models","created":"2026-05-25","cited_by_count":11,"area":"vla"},{"id":"2602.02459","title":"TIC-VLA: A Think-in-Control Vision-Language-Action Model for Robot Navigation in Dynamic Environments","created":"2026-02-02","cited_by_count":10,"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":"2510.12710","title":"Reflection-Based Task Adaptation for Self-Improving VLA","created":"2025-10-14","cited_by_count":8,"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":"2508.15201","title":"Survey of Vision-Language-Action Models for Embodied Manipulation","created":"2025-08-21","cited_by_count":7,"area":"vla"},{"id":"2602.05765","title":"RL-VLA$^3$: A Flexible and Asynchronous Reinforcement Learning Framework for VLA Training","created":"2026-02-05","cited_by_count":7,"area":"vla"},{"id":"2602.12691","title":"ALOE: Action-Level Off-Policy Evaluation for Vision-Language-Action Model Post-Training","created":"2026-02-13","cited_by_count":7,"area":"vla"},{"id":"2510.17640","title":"RESample: A Robust Data Augmentation Framework via Exploratory Sampling for Robotic Manipulation","created":"2025-10-20","cited_by_count":6,"area":"vla"},{"id":"2602.09023","title":"TwinRL: Digital Twin-Driven Reinforcement Learning for Real-World Robotic Manipulation","created":"2026-02-09","cited_by_count":6,"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":"2605.10821","title":"UniSteer: Unified Noise Steering for Efficient Human-Guided VLA Adaptation","created":"2026-05-11","cited_by_count":6,"area":"vla"},{"id":"2508.05186","title":"Learning to See and Act: Task-Aware Virtual View Exploration for Robotic Manipulation","created":"2025-08-07","cited_by_count":5,"area":"vla"},{"id":"2511.18082","title":"ActDistill: General Action-Guided Self-Derived Distillation for Efficient Vision-Language-Action Models","created":"2025-11-22","cited_by_count":5,"area":"vla"},{"id":"2512.24210","title":"GR-Dexter Technical Report","created":"2025-12-30","cited_by_count":5,"area":"vla"},{"id":"2602.03983","title":"Efficient Long-Horizon Vision-Language-Action Models via Static-Dynamic Disentanglement","created":"2026-02-03","cited_by_count":5,"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":"2605.17517","title":"AffordVLA: Injecting Affordance Representations into Vision-Language-Action Models via Implicit Feature Alignment","created":"2026-05-17","cited_by_count":5,"area":"vla"},{"id":"2510.14902","title":"VLA^2: Empowering Vision-Language-Action Models with an Agentic Framework for Unseen Concept Manipulation","created":"2025-10-16","cited_by_count":4,"area":"vla"},{"id":"2512.11362","title":"An Anatomy of Vision-Language-Action Models: From Modules to Milestones and Challenges","created":"2025-12-12","cited_by_count":4,"area":"vla"},{"id":"2602.10698","title":"AugVLA-3D: Depth-Driven Feature Augmentation for Vision-Language-Action Models","created":"2026-02-11","cited_by_count":4,"area":"vla"},{"id":"2602.21633","title":"Self-Correcting VLA: Online Action Refinement via Sparse World Imagination","created":"2026-02-25","cited_by_count":4,"area":"vla"},{"id":"2604.04834","title":"E-VLA: Event-Augmented Vision-Language-Action Model for Dark and Blurred Scenes","created":"2026-04-06","cited_by_count":4,"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":"2607.29613","title":"WCM: A World Critic Model for Vision-Language-Action Reinforcement Learning","created":"2026-07-31","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":"2511.19528","title":"Discover, Learn, and Reinforce: Scaling Vision-Language-Action Pretraining with Diverse RL-Generated Trajectories","created":"2025-11-24","cited_by_count":3,"area":"vla"},{"id":"2603.11080","title":"SELF-VLA: A Skill Enhanced Agentic Vision-Language-Action Framework for Contact-Rich Disassembly","created":"2026-03-10","cited_by_count":3,"area":"vla"},{"id":"2604.21391","title":"From Noise to Intent: Anchoring Generative VLA Policies with Residual Bridges","created":"2026-04-23","cited_by_count":3,"area":"vla"},{"id":"2605.11832","title":"Learning Action Manifold with Multi-view Latent Priors for Robotic Manipulation","created":"2026-05-12","cited_by_count":3,"area":"vla"},{"id":"2606.31958","title":"Adapting Generalist Robot Policies with Semantic Reinforcement Learning","created":"2026-06-30","cited_by_count":3,"area":"vla"},{"id":"2511.22555","title":"Beyond Success: Refining Elegant Robot Manipulation from Mixed-Quality Data via Just-in-Time Intervention","created":"2025-11-27","cited_by_count":2,"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":"2605.17486","title":"DyGRO-VLA: Cross-Task Scaling of Vision-Language-Action Models via Dynamic Grouped Residual Optimization","created":"2026-05-17","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":"2606.26006","title":"FORCE: Efficient VLA Reinforcement Fine-Tuning via Value-Calibrated Warm-up and Self-Distillation","created":"2026-06-24","cited_by_count":2,"area":"vla"},{"id":"2606.31846","title":"Z-1: Efficient Reinforcement Learning for Vision-Language-Action Models","created":"2026-06-30","cited_by_count":2,"area":"vla"},{"id":"2607.12931","title":"ExToken: Structured Exploration for Efficient Vision-Language-Action Reinforcement Fine-tuning","created":"2026-07-14","cited_by_count":2,"area":"vla"},{"id":"2608.23831","title":"Learning to Act While Waiting: RL Finetuning of Generalist Robot Policies Under Inference Latency","created":"2026-08-24","cited_by_count":2,"area":"vla"},{"id":"2609.18207","title":"Reinforcement Learning for Real-Time Vision-Language-Action Policies","created":"2026-09-16","cited_by_count":2,"area":"vla"},{"id":"2606.04968","title":"Potential-Guided Flow Matching for Vision-Language-Action Policy Improvement","created":"2026-06-03","cited_by_count":1,"area":"vla"},{"id":"2606.13886","title":"PhysVLA: Towards Physically-Grounded VLA for Embodied Robotic Manipulation","created":"2026-06-11","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":"2606.23623","title":"dVLA-RL: Reinforcement Learning over Denoising Trajectories for Discrete Diffusion Vision-Language-Action Models","created":"2026-06-22","cited_by_count":1,"area":"vla"},{"id":"2606.29892","title":"Trust Your Instincts: Confidence-Driven Test-Time RL for Vision-Language-Action Models","created":"2026-06-29","cited_by_count":1,"area":"vla"},{"id":"2608.15026","title":"PACE: Phase-Progress-Aware Credit for Long-Horizon Embodied Manipulation","created":"2026-08-15","cited_by_count":1,"area":"vla"},{"id":"2603.05147","title":"Act, Think or Abstain: Complexity-Aware Adaptive Inference for Vision-Language-Action Models","created":"2026-03-05","cited_by_count":0,"area":"vla"},{"id":"2604.01570","title":"Boosting Vision-Language-Action Finetuning with Feasible Action Neighborhood Prior","created":"2026-04-02","cited_by_count":0,"area":"vla"},{"id":"2604.17706","title":"OmniVLA-RL: A Vision-Language-Action Model with Spatial Understanding and Online RL","created":"2026-04-20","cited_by_count":0,"area":"vla"},{"id":"2606.17011","title":"ROVE: Unlocking Human Interventions for Humanoid Manipulation via Reinforcement Learning","created":"2026-06-15","cited_by_count":0,"area":"vla"},{"id":"2607.06706","title":"Vision Language Action (VLA) Models for Unmanned Aerial Robotics and Bimanual Manipulation: A Review","created":"2026-07-07","cited_by_count":0,"area":"vla"},{"id":"2608.07314","title":"TEMPO: Semantic-Action Decoupled RL Post-Training for Vision-Language-Action Models","created":"2026-08-07","cited_by_count":0,"area":"vla"},{"id":"2608.27079","title":"GRAFT: Grounded and Efficient Online Reinforcement Adaptation for Fine-Grained Robot Manipulation","created":"2026-08-27","cited_by_count":0,"area":"vla"},{"id":"2609.04355","title":"VLA-Precision: Asymmetric Co-Bootstrapping for Efficient Real-World Online RL of Vision-Language-Action Models","created":"2026-09-03","cited_by_count":0,"area":"vla"},{"id":"2609.18392","title":"DistAL: Distance-based Advantage Learning for VLA Fine-Tuning","created":"2026-09-16","cited_by_count":0,"area":"vla"},{"id":"2609.21650","title":"SynthDemo-RL: Breaking the Zero-Reward Barrier in VLA Adaptation with LLM-Guided Synthetic Demonstrations","created":"2026-09-18","cited_by_count":0,"area":"vla"},{"id":"2609.27450","title":"BEE: Intervention-Adaptive Real-World Reinforcement Learning with Vision-Language-Action Models","created":"2026-09-23","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":"2609.36588","title":"Cooperative Multi-Agent Vision-Language-Action Models via Reinforced Fine Tuning","created":"2026-09-29","cited_by_count":0,"area":"vla"},{"id":"2609.38855","title":"Online Evolution Strategy for Flow-Matching VLA Policies via Self-Supervised Trajectory Distribution Optimization","created":"2026-09-30","cited_by_count":0,"area":"vla"},{"id":"2609.39198","title":"DSDyn-VLA: A Dual-Stream Dynamic Manipulation Framework with Motion Perception, Future Awareness, and Realtime Correction","created":"2026-09-30","cited_by_count":0,"area":"vla"},{"id":"2610.00317","title":"DriftOPD: Sequence-Level Reverse-KL Distillation for One-Step VLA Policies","created":"2026-09-29","cited_by_count":0,"area":"vla"},{"id":"2610.05994","title":"How (and How Not) to Use Data Augmentation in VLA Post-Training","created":"2026-10-05","cited_by_count":0,"area":"vla"},{"id":"2610.09943","title":"Many Ways to Succeed: Diversity-Driven RL Fine-Tuning for VLA Generalization","created":"2026-10-07","cited_by_count":0,"area":"vla"},{"id":"2610.11971","title":"CAPABLE: Capability-Aware Policy Adaptation via Behavioral Latent Encoding","created":"2026-10-08","cited_by_count":0,"area":"vla"}],"next_cursor":null}