Dissecting Advantage-Guided Post-Training for Vision-Language-Action Policies
cs.RO
Submitted: 2026-09-23
Updated: 2026-09-23
Code: https://github.com/huggingface/lerobot
Terminology
Sources
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
- OpenVLA: An Open-Source Vision-Language-Action Model
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- Interactive Post-Training for Vision-Language-Action Models
- TGRPO :Fine-tuning Vision-Language-Action Model via Trajectory-wise Group Relative Policy Optimization
- ALOE: Action-Level Off-Policy Evaluation for Vision-Language-Action Model Post-Training
- RFTF: Reinforcement Fine-tuning for Embodied Agents with Temporal Feedback
- Using Non-Expert Data to Robustify Imitation Learning via Offline Reinforcement Learning
- CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning
- ARM: Advantage Reward Modeling for Long-Horizon Manipulation
- Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
- AWAC: Accelerating Online Reinforcement Learning with Offline Datasets
- $\pi^{*}_{0.6}$: a VLA That Learns From Experience
- Proximal Policy Optimization Algorithms
- Stratified GRPO: Handling Structural Heterogeneity in Reinforcement Learning of LLM Search Agents
- SmolVLM: Redefining small and efficient multimodal models
- Hierarchical Advantage Weighting for Online RL Fine-Tuning of VLAs from Sparse Episode Outcomes
- Learning While Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies
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