EXPO-FT: Sample-Efficient Reinforcement Learning Fine-Tuning for Vision-Language-Action Models

from Embodied AI 101 · host Shaoqing Tan

Extends the EXPO method with real-world RL post-training for VLAs using image observations, action chunking, DAgger, and on-the-fly Q-value maximization. Achieves 30/30 success on 8 challenging manipulation tasks with only ~19 min of RL data on average.

Episode metadata supplied by the publisher feed · Published May 29, 2026

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EXPO-FT: Sample-Efficient Reinforcement Learning Fine-Tuning for Vision-Language-Action Models

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