VLA models exhibit layer-wise redundancy allowing up to 50% depth compression via training-free CKA-based removal, yielding faster fine-tuning and inference with no performance loss on robot tasks.
arXiv preprint arXiv:2601.19634 (2026)
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FASTER adds a Horizon-Aware Schedule to flow VLAs that compresses immediate-action denoising to one step while keeping long-horizon trajectory quality, lowering real-robot reaction latency.
ElegantVLA accelerates VLA models up to 3.77x by dynamically scheduling compute across vision, language, and action components without retraining the base model.
citing papers explorer
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Finetuning Vision-Language-Action Models Requires Fewer Layers Than You Think
VLA models exhibit layer-wise redundancy allowing up to 50% depth compression via training-free CKA-based removal, yielding faster fine-tuning and inference with no performance loss on robot tasks.
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FASTER: Rethinking Real-Time Flow VLAs
FASTER adds a Horizon-Aware Schedule to flow VLAs that compresses immediate-action denoising to one step while keeping long-horizon trajectory quality, lowering real-robot reaction latency.
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ElegantVLA: Learning When to Think for Efficient Vision-Language-Action Models
ElegantVLA accelerates VLA models up to 3.77x by dynamically scheduling compute across vision, language, and action components without retraining the base model.