FT-NCFM：効率的なVLAモデルのための影響力考慮型データ蒸留フレームワーク
FT-NCFM: An Influence-Aware Data Distillation Framework for Efficient VLA Models
VLAモデルの学習データを因果帰属と敵対的生成で蒸留し、5%のデータで85-90%の成功率を達成しつつ学習時間を80%以上削減するデータ中心の手法を提案。
詳しい要約
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著者: Kewei Chen, Yayu Long, Shuai Li, Mingsheng Shang
分類: cs.RO
原文アブストラクト
The powerful generalization of Vision-Language-Action (VLA) models is bottlenecked by their heavy reliance on massive, redundant, and unevenly valued datasets, hindering their widespread application. Existing model-centric optimization paths, such as model compression (which often leads to performance degradation) or policy distillation (whose products are model-dependent and lack generality), fail to fundamentally address this data-level challenge. To this end, this paper introduces FT-NCFM, a fundamentally different, data-centric generative data distillation framework. Our framework employs a self-contained Fact-Tracing (FT) engine that combines causal attribution with programmatic contrastive verification to assess the intrinsic value of samples. Guided by these assessments, an adversarial NCFM process synthesizes a model-agnostic, information-dense, and reusable data asset. Experimental results on several mainstream VLA benchmarks show that models trained on just 5% of our distilled coreset achieve a success rate of 85-90% compared with training on the full dataset, while reducing training time by over 80%. Our work demonstrates that intelligent data distillation is a highly promising new path for building efficient, high-performance VLA models.
