arXiv AI
VLANeXt: Recipes for Building Strong VLA Models
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arXiv AI
3d ago
VLANeXt Family: A Systematic Study of VLA Models from Core Recipes to Emerging Paradigms
The paper presents VLANeXt, a Vision‑Language‑Action (VLA) model built from a unified framework that systematically analyzes design choices across foundational components, perception essentials, and action modeling. By dissecting these dimensions, the authors identify 12 key findings that form a practical recipe for strong VLA models, and demonstrate that VLANeXt outperforms state‑of‑the‑art methods on LIBERO and LIBERO‑plus benchmarks while also excelling in real‑world experiments. The study further extends VLANeXt into a family of variants—scaled models, latent‑action pretraining, latent predictive representation learning, and world action modeling—showing that the core recipe remains effective across different scales and emerging paradigms.
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 18
StarVLA-$\alpha$: Reducing Complexity in Vision-Language-Action Systems
StarVLA-α is a streamlined Vision‑Language‑Action (VLA) model that reduces architectural and pipeline complexity to facilitate systematic study of VLA design choices. By employing a strong VLM backbone and minimal design, it achieves competitive performance across multiple benchmarks (LIBERO, SimplerEnv, RoboTwin, RoboCasa) and outperforms the baseline π₀.₅ by 20% on the RoboChallenge benchmark. The authors plan to release the code to support future VLA research.
arXiv Machine Learning
Jun 18
Does VLA Even Know the Basics? Measuring Commonsense and World Knowledge Retention in Vision-Language-Action Models
arXiv:2606. 19297v1 Announce Type: new Abstract: Embodied Vision-Language-Action (VLA) models are typically obtained by fine-tuning powerful pretrained VLMs on robotics data, yet it is unclear how much commonsense and factual knowledge they retain after adaptation.
Hugging Face Trending Papers
Jun 17
Does VLA Even Know the Basics? Measuring Commonsense and World Knowledge Retention in Vision-Language-Action Models
Embodied Vision-Language-Action (VLA) models are typically obtained by fine-tuning powerful pretrained VLMs on robotics data, yet it is unclear how much commonsense and factual knowledge they retain after adaptation. Failures on knowledge-sensitive tasks are ambiguous, conflating missing knowledge with poor generalization of low-level control.
arXiv AI
1d ago
The Layer Mystery of VLA: An Information-Theoretical Analysis of VLA Latent Interface
arXiv:2609.36118v1 Announce Type: new Abstract: Vision-language-action (VLA) policies connect a pretrained vision-language backbone to an action head through a latent interface, but which backbone la...