DistAL: Distance-based Advantage Learning for VLA Fine-Tuning
cs.RO
Submitted: 2026-09-16
Updated: 2026-09-16
Terminology
Sources
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
- OpenVLA: An Open-Source Vision-Language-Action Model
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- Flow Matching for Generative Modeling
- RT-1: Robotics Transformer for Real-World Control at Scale
- Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning
- $\pi^{*}_{0.6}$: a VLA That Learns From Experience
- Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment
- Multi-Task Interactive Robot Fleet Learning with Visual World Models
- Policy Agnostic RL: Offline RL and Online RL Fine-Tuning of Any Class and Backbone
- ConRFT: A Reinforced Fine-tuning Method for VLA Models via Consistency Policy
- Improving Vision-Language-Action Model with Online Reinforcement Learning
- Interactive Post-Training for Vision-Language-Action Models
- VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning
- RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
- RL Token: Bootstrapping Online RL with Vision-Language-Action Models
- Diffusion Guidance Is a Controllable Policy Improvement Operator
- Classifier-Free Diffusion Guidance
- Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
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