arXiv AI
The Layer Mystery of VLA: An Information-Theoretical Analysis of VLA Latent Interface
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arXiv AI
Jun 9
CrossVLA: Cross-Paradigm Post-Training and Inference Optimization for Vision-Language-Action Models
arXiv:2605. 21854v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have rapidly converged on a small set of architectural patterns: discrete-token autoregression (e.
arXiv AI
Jun 2
VLM4VLA: Revisiting Vision-Language-Models in Vision-Language-Action Models
arXiv:2601. 03309v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models, which integrate pretrained large Vision-Language Models (VLM) into their policy backbone, are gaining significant attention for their promising generalization capabilities.
arXiv AI
Sep 10
LayerRoute: Action-Conditioned Mixture-of-Layers Routing for Vision-Language-Action Policies
arXiv:2609.06079v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies leverage pretrained vision-language models (VLMs) to guide action generation for robot control. VLMs provide hier...
arXiv AI
Aug 26
VLANeXt: Recipes for Building Strong VLA Models
arXiv:2602.18532v3 Announce Type: replace-cross Abstract: Following the rise of large foundation models, Vision-Language-Action models (VLAs) emerged, leveraging strong visual and language understand...
arXiv AI
Aug 18
FabriMAE I Trust Myself? Self-Evaluating VLA Action Generation with Markov Attention Entropy
arXiv:2608. 16697v1 Announce Type: new Abstract: Vision-Language-Action models (VLAs) integrate visual perception, language instruction, and action generation into end-to-end policies across heterogeneous architectures.
arXiv Machine Learning
6d ago
Decoupled Early Exits for Task-Dependent Compute Allocation in Flow-Matching VLAs
The paper introduces a framework for Flow‑Matching Vision‑Language‑Action (VLA) models that allows independent adjustment of backbone depth, action expert depth, and denoising steps. Lightweight Exit Transformers are added at intermediate layers to enable early exits, and a KV Cache synthesis mechanism manages skipped layers so the action expert can exit deeper than the backbone. Experiments on SmolVLA and π0.5 across LIBERO and Meta‑World show that joint tuning of these compute axes reduces latency by 79.2 % and FLOPs by 31.8 %, while improving mean success rate by 5.6 %.