{"total":154,"rows":[{"id":"2506.01844","title":"SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics","created":"2025-06-02","cited_by_count":530,"area":"mllm"},{"id":"2511.14759","title":"$\\pi^{*}_{0.6}$: a VLA That Learns From Experience","created":"2025-11-18","cited_by_count":303,"area":"vla"},{"id":"2509.08827","title":"A Survey of Reinforcement Learning for Large Reasoning Models","created":"2025-09-10","cited_by_count":180,"area":"lr"},{"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":"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":"2510.07077","title":"Vision-Language-Action Models for Robotics: A Review Towards Real-World Applications","created":"2025-10-08","cited_by_count":84,"area":"mllm"},{"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":"2505.04769","title":"Vision-Language-Action (VLA) Models: Concepts, Progress, Applications and Challenges","created":"2025-05-07","cited_by_count":74,"area":"mllm"},{"id":"2509.15937","title":"A Vision-Language-Action-Critic Model for Robotic Real-World Reinforcement Learning","created":"2025-09-19","cited_by_count":71,"area":"vla"},{"id":"2511.09515","title":"WMPO: World Model-based Policy Optimization for Vision-Language-Action Models","created":"2025-11-12","cited_by_count":70,"area":"wm"},{"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":"2510.00406","title":"VLA-RFT: Vision-Language-Action Reinforcement Fine-tuning with Verified Rewards in World Simulators","created":"2025-10-01","cited_by_count":67,"area":"wm"},{"id":"2602.13977","title":"WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL","created":"2026-02-15","cited_by_count":50,"area":"wm"},{"id":"2505.21906","title":"ChatVLA-2: Vision-Language-Action Model with Open-World Embodied Reasoning from Pretrained Knowledge","created":"2025-05-28","cited_by_count":49,"area":"lr"},{"id":"2506.08440","title":"TGRPO :Fine-tuning Vision-Language-Action Model via Trajectory-wise Group Relative Policy Optimization","created":"2025-06-10","cited_by_count":49,"area":"vla"},{"id":"2509.19012","title":"Pure Vision Language Action (VLA) Models: A Comprehensive Survey","created":"2025-09-23","cited_by_count":49,"area":"mllm"},{"id":"2604.23073","title":"RL Token: Bootstrapping Online RL with Vision-Language-Action Models","created":"2026-04-24","cited_by_count":49,"area":"vla"},{"id":"2512.23703","title":"Robo-Dopamine: General Process Reward Modeling for High-Precision Robotic Manipulation","created":"2025-12-29","cited_by_count":40,"area":"lr"},{"id":"2507.10672","title":"Vision Language Action Models in Robotic Manipulation: A Systematic Review","created":"2025-07-14","cited_by_count":38,"area":"vla"},{"id":"2508.13998","title":"Embodied-R1: Reinforced Embodied Reasoning for General Robotic Manipulation","created":"2025-08-19","cited_by_count":38,"area":"lr"},{"id":"2508.02219","title":"CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning","created":"2025-08-04","cited_by_count":37,"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":"2602.19313","title":"TOPReward: Token Probabilities as Hidden Zero-Shot Rewards for Robotics","created":"2026-02-22","cited_by_count":33,"area":"alignment"},{"id":"2508.03645","title":"DiWA: Diffusion Policy Adaptation with World Models","created":"2025-08-05","cited_by_count":31,"area":"wm"},{"id":"2506.16685","title":"Compliant Residual DAgger: Improving Real-World Contact-Rich Manipulation with Human Corrections","created":"2025-06-20","cited_by_count":29,"area":null},{"id":"2510.17111","title":"Efficient Vision-Language-Action Models for Embodied Manipulation: A Systematic Survey","created":"2025-10-20","cited_by_count":26,"area":"mllm"},{"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":"2505.20024","title":"ReasonPlan: Unified Scene Prediction and Decision Reasoning for Closed-loop Autonomous Driving","created":"2025-05-26","cited_by_count":22,"area":"lr"},{"id":"2510.14836","title":"QDepth-VLA: Quantized Depth Prediction as Auxiliary Supervision for Vision-Language-Action Models","created":"2025-10-16","cited_by_count":21,"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":"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":"2511.15669","title":"DeepThinkVLA: Enhancing Reasoning Capability of Vision-Language-Action Models","created":"2025-10-31","cited_by_count":18,"area":"lr"},{"id":"2506.20966","title":"Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends","created":"2025-06-26","cited_by_count":17,"area":"mllm"},{"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":"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":"2510.07730","title":"DEAS: DEtached value learning with Action Sequence for Scalable Offline RL","created":"2025-10-09","cited_by_count":10,"area":"offlinerl"},{"id":"2508.08189","title":"Reinforcement Learning for Large Model: A Survey","created":"2025-08-11","cited_by_count":9,"area":"deeprl"},{"id":"2505.19769","title":"TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning","created":"2025-05-26","cited_by_count":8,"area":"diffusion"},{"id":"2510.12710","title":"Reflection-Based Task Adaptation for Self-Improving VLA","created":"2025-10-14","cited_by_count":8,"area":"vla"},{"id":"2512.16911","title":"Posterior Behavioral Cloning: Pretraining BC Policies for Efficient RL Finetuning","created":"2025-12-18","cited_by_count":8,"area":"offlinerl"},{"id":"2512.24288","title":"Real-world Reinforcement Learning from Suboptimal Interventions","created":"2025-12-30","cited_by_count":8,"area":"deeprl"},{"id":"2604.28192","title":"LaST-R1: Reinforcing Robotic Manipulation via Adaptive Physical Latent Reasoning","created":"2026-04-30","cited_by_count":8,"area":null},{"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":"2509.19292","title":"SOE: Sample-Efficient Robot Policy Self-Improvement via On-Manifold Exploration","created":"2025-09-23","cited_by_count":6,"area":null},{"id":"2510.02665","title":"Self-Improvement in Multimodal Large Language Models: A Survey","created":"2025-10-03","cited_by_count":6,"area":"mllm"},{"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":"2603.09121","title":"DexHiL: A Human-in-the-Loop Framework for Vision-Language-Action Model Post-Training in Dexterous Manipulation","created":"2026-03-10","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":"2606.12372","title":"UniIntervene: Agentic Intervention for Efficient Real-World Reinforcement Learning","created":"2026-06-10","cited_by_count":6,"area":"agents"},{"id":"2507.04452","title":"SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training","created":"2025-07-06","cited_by_count":5,"area":"deeprl"},{"id":"2509.24524","title":"PhysiAgent: An Embodied Agent Framework in Physical World","created":"2025-09-29","cited_by_count":5,"area":"vla"},{"id":"2512.21586","title":"Videos are Sample-Efficient Supervisions: Behavior Cloning from Videos via Latent Representations","created":"2025-12-25","cited_by_count":5,"area":null},{"id":"2601.19969","title":"E2HiL: Entropy-Guided Sample Selection for Efficient Real-World Human-in-the-Loop Reinforcement Learning","created":"2026-01-27","cited_by_count":5,"area":"deeprl"},{"id":"2602.07399","title":"VGAS: Value-Guided Action-Chunk Selection for Few-Shot Vision-Language-Action Adaptation","created":"2026-02-07","cited_by_count":5,"area":"lr"},{"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":"2605.10094","title":"Retrieve-then-Steer: Online Success Memory for Test-Time Adaptation of Generative VLAs","created":"2026-05-11","cited_by_count":5,"area":"vla"},{"id":"2608.21204","title":"Beyond Imitation: Self-Improving Robot Policies via Off-Policy Q-Planning","created":"2026-08-21","cited_by_count":5,"area":null},{"id":"2506.09655","title":"DipLLM: Fine-Tuning LLM for Strategic Decision-making in Diplomacy","created":"2025-06-11","cited_by_count":4,"area":null},{"id":"2509.25718","title":"VLA Model Post-Training via Action-Chunked PPO and Self Behavior Cloning","created":"2025-09-30","cited_by_count":4,"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":"2603.20607","title":"Towards Practical World Model-based Reinforcement Learning for Vision-Language-Action Models","created":"2026-03-21","cited_by_count":4,"area":"wm"},{"id":"2606.09337","title":"TORL-VLA: Tactile Guided Online Reinforcement Learning for Contact-Rich Manipulation","created":"2026-06-08","cited_by_count":4,"area":"vla"},{"id":"2606.17906","title":"WAM-RL: World-Action Model Reinforcement Learning with Reconstruction Rewards and Online Video SFT","created":"2026-06-16","cited_by_count":4,"area":"wm"},{"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":"2607.03751","title":"Look Before You Leap: Distilling Tree Search into Action Evaluation for Frozen VLA Models","created":"2026-07-04","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":"2510.23576","title":"UrbanVLA: A Vision-Language-Action Model for Urban Micromobility","created":"2025-10-27","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":"2603.28730","title":"SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot Reinforcement Learning","created":"2026-03-30","cited_by_count":3,"area":"lr"},{"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":"2607.02431","title":"WorldSample: Closed-loop Real-robot RL with World Modelling","created":"2026-07-02","cited_by_count":3,"area":"wm"},{"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":"2602.12636","title":"Dual-Granularity Contrastive Reward via Generated Episodic Guidance for Efficient Embodied RL","created":"2026-02-13","cited_by_count":2,"area":"deeprl"},{"id":"2603.05296","title":"Latent Policy Steering through One-Step Flow Policies","created":"2026-03-05","cited_by_count":2,"area":"offlinerl"},{"id":"2603.18532","title":"Scaling Sim-to-Real VLA Reinforcement Learning with Generative 3D Worlds","created":"2026-03-19","cited_by_count":2,"area":"mllm"},{"id":"2605.11151","title":"RankQ: Offline-to-Online Reinforcement Learning via Self-Supervised Action Ranking","created":"2026-05-11","cited_by_count":2,"area":"offlinerl"},{"id":"2605.12167","title":"From Imagined Futures to Executable Actions: Mixture of Latent Actions for Robot Manipulation","created":"2026-05-12","cited_by_count":2,"area":null},{"id":"2605.15157","title":"Hand-in-the-Loop: Improving VLA Policies for Dexterous Manipulation via Seamless Hand-Arm Intervention","created":"2026-05-14","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":"2606.03949","title":"Preference-Calibrated Human-in-the-Loop Reinforcement Learning for Robotic Manipulation","created":"2026-06-02","cited_by_count":2,"area":"deeprl"},{"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":"2606.10305","title":"SARM2: Multi-Task Stage Aware Reward Modeling for Self Improving Robotic Manipulation","created":"2026-06-09","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":"2606.26801","title":"Improving Vision-Language-Action Model Fine-Tuning with Structured Stage and Keyframe Supervision","created":"2026-06-25","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":"2607.27203","title":"Do You Really Need to Pretrain Q-Functions for Online RL Fine-Tuning?","created":"2026-07-29","cited_by_count":2,"area":"deeprl"},{"id":"2608.13026","title":"Temporal GRPO: Beyond Trajectory-Level Credit in Vision-Language-Action Reinforcement Learning","created":"2026-08-13","cited_by_count":2,"area":"lr"}],"next_cursor":"100"}