ActDistill: General Action-Guided Self-Derived Distillation for Efficient Vision-Language-Action Models

• URL: http://arxiv.org/abs/2511.18082v1
• Date: Sat, 22 Nov 2025 14:44:03 GMT
• Title: ActDistill: General Action-Guided Self-Derived Distillation for Efficient Vision-Language-Action Models
• Authors: Wencheng Ye, Tianshi Wang, Lei Zhu, Fengling Li, Guoli Yang,
• Abstract summary: We present ActDistill, a framework that transfers the action prediction capability of any existing VLA model to a lightweight counterpart.<n>We employ a well-trained VLA model as the teacher and introduce a graph-structured encapsulation strategy to explicitly model the hierarchical evolution of action prediction.<n>Experiments on embodied benchmarks demonstrate that ActDistill achieves comparable or superior performance to full-scale VLA models while reducing computation by over 50% with up to 1.67 times speedup.
• Score: 14.202025149504715
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent Vision-Language-Action (VLA) models have shown impressive flexibility and generalization, yet their deployment in robotic manipulation remains limited by heavy computational overhead and inference latency. In this work, we present ActDistill, a general action-guided self-derived distillation framework that transfers the action prediction capability of any existing VLA model to a lightweight counterpart. Unlike previous efficiency strategies that primarily emphasize vision-language correlations, ActDistill leverages action priors to guide knowledge transfer and model compression, achieving action-oriented efficiency for VLA models. Specifically, we employ a well-trained VLA model as the teacher and introduce a graph-structured encapsulation strategy to explicitly model the hierarchical evolution of action prediction. The student model, derived from the graph-encapsulated teacher, is further equipped with a dynamic router that adaptively selects computation paths based on action prediction demands, guided by hierarchical graph-informed supervision to ensure smooth and efficient evolution. During inference, graph-related auxiliary components are removed, allowing the student to execute only dynamically routed layers and predict high-precision actions with minimal computation and latency. Experiments on embodied benchmarks demonstrate that ActDistill achieves comparable or superior performance to full-scale VLA models while reducing computation by over 50% with up to 1.67 times speedup, thereby establishing a general paradigm toward efficient embodied intelligence.

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