LEAP-VLA: Latent-Enhanced Action Prototyping via Continuous Residual Latent Spaces for Vision-Language-Action Models

Vision-Language-Action (VLA) models have advancedrapidly, yet most gains still come from larger backbones, larger embod-ied datasets, or expensive iterative decoders, while action representa-tion remains under-explored. We argue that action space design is aprimary bottleneck and present LEAP (Latent-Enhanced ActionPrototyping)-VLA, a two-stage framework that learns a structuredaction latent space from demonstrations via Multi-level Soft ResidualQuantization (MSRQ), then trains a lightweight VLM-external aligner topredict actions by prototype-aware soft selection in that space. This de-sign replaces discrete code assignment and multi-step denoising with fullydifferentiable single-pass latent prediction, preserving pretrained vision-language alignment without modifying the VLM. Empirically, LEAP-VLA outperforms the state-of-the-art methods with substantially fewertrainable parameters and no embodied pretraining, showing that a well-structured continuous action latent space can offset model scale and datarequirements.