Learning to Accelerate Vision-Language-Action Models through Adaptive Visual Token Caching

• URL: http://arxiv.org/abs/2602.00686v1
• Date: Sat, 31 Jan 2026 12:12:51 GMT
• Title: Learning to Accelerate Vision-Language-Action Models through Adaptive Visual Token Caching
• Abstract summary: We reformulate inference acceleration as a learnable policy optimization problem.<n>We propose a novel framework that integrates a task-aware decision-making process directly into the Vision-Language-Action model.<n>Our method achieves a 1.76x wall-clock inference speedup while simultaneously improving the average success rate.
• Score: 25.295588774254952
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable generalization capabilities in robotic manipulation tasks, yet their substantial computational overhead remains a critical obstacle to real-world deployment. Improving inference efficiency is therefore essential for practical robotic applications. Existing acceleration methods often rely on heuristic or static strategies--such as rule-based token caching or pruning--that are decoupled from task objectives and fail to adapt to dynamic scene changes. In this work, we reformulate inference acceleration as a learnable policy optimization problem and propose a novel framework that integrates a dynamic, task-aware decision-making process directly into the VLA model. At its core are two lightweight, cooperative modules: a Cached Token Selector, which determines which tokens should be reused, and a Cache Ratio Predictor, which controls how many tokens to reuse. Training these modules is non-trivial due to their discrete decisions. We address this by adopting a differentiable relaxation that allows gradient-based end-to-end optimization. Extensive experiments on the LIBERO and SIMPLER benchmarks, as well as real-robot evaluations, show that our method achieves a 1.76x wall-clock inference speedup while simultaneously improving the average success rate by 1.9 percentage points (from 75.0% to 76.9%) on LIBERO and by 5.0 percentage points on real-world tasks, significantly outperforming existing baselines. This work highlights the potential of learning task-aware computational allocation policies, paving the way for VLA models that are both powerful and efficient.

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