視覚言語行動ポリシーのためのアドバンテージ誘導事後学習の分解
Dissecting Advantage-Guided Post-Training for Vision-Language-Action Policies
視覚言語行動ポリシーの事後学習において、アドバンテージ誘導強化学習の設計選択を分解し、段階的評価で有効なモジュール式レシピを特定した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Jiahang Cao, Hanye Zhao, Hang Lai, Shenyu Zhang, Xiaoshen Han, Xinghang Li, Futeng Liu, Wanli Peng, Heyun Wang, Yunhong Wang, Jason Li, Yong Yu, Weinan Zhang
分類: cs.RO
原文アブストラクト
Advantage-guided reinforcement learning provides a practical way to post-train vision-language-action (VLA) policies using limited robot data. However, its performance depends on several coupled choices, including how critic-derived advantages are constructed, calibrated, and used for policy training. Existing recipes often combine these choices into a single end-to-end procedure, making their individual effects difficult to identify. In this work, we dissect advantage-guided VLA post-training through a controlled empirical study that separates these design choices while accounting for their distinct estimands. We develop stage-specific offline evaluation methods to screen alternative choices efficiently, without requiring extensive real-robot policy evaluations for every possible combination. The staged evaluation identifies a modular recipe that combines temporal-difference advantage construction, group-wise calibration, and continuous advantage weighting. Across four real-world bimanual tasks, the resulting recipe improves mean task progress and success over the SFT initialization by 0.42 and 0.63, respectively. Moreover, the proposed evaluation diagnostics show an overall alignment with downstream real-world performance, supporting their use for interpreting empirical outcomes and selecting advantage-guided post-training designs in practice.