KV-Efficient VLA: A Method of Speed up Vision Language Model with RNN-Gated Chunked KV Cache

• URL: http://arxiv.org/abs/2509.21354v1
• Date: Sat, 20 Sep 2025 02:04:24 GMT
• Title: KV-Efficient VLA: A Method of Speed up Vision Language Model with RNN-Gated Chunked KV Cache
• Authors: Wanshun Xu, Long Zhuang,
• Abstract summary: Vision-Language-Action (VLA) models promise unified robotic perception and control, yet their scalability is constrained by the quadratic cost of attention and the unbounded growth of key-value (KV) memory during long-horizon inference.<n>We present KV-Efficient VLA, a model-agnostic memory compression framework that addresses these limitations by introducing a lightweight, training-friendly mechanism to selectively retain high-utility context.<n>Our method integrates seamlessly into existing autoregressive and hybrid VLA stacks, enabling scalable inference without modifying training pipelines or downstream control logic.
• Score: 0.9238700679836854
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language-Action (VLA) models promise unified robotic perception and control, yet their scalability is constrained by the quadratic cost of attention and the unbounded growth of key-value (KV) memory during long-horizon inference. While recent methods improve generalization through scaling backbone architectures, they often neglect the inference inefficiencies critical to real-time deployment. In this work, we present KV-Efficient VLA, a model-agnostic memory compression framework that addresses these limitations by introducing a lightweight, training-friendly mechanism to selectively retain high-utility context. Our method partitions the KV cache into fixed size chunks and employs a recurrent gating module to summarize and filter historical context according to learned utility scores. This design preserves recent fine-grained detail while aggressively pruning stale, low-relevance memory, all while maintaining causality. Theoretically, KV-Efficient VLA yields up to 1.21x inference speedup and 36% KV memory reduction, with minimal impact on task success. Our method integrates seamlessly into existing autoregressive and hybrid VLA stacks, enabling scalable inference without modifying training pipelines or downstream control logic.

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