arXiv AI
VLA-Retain: Benchmarking Cross-Dimension Effects of Vision Tuning in VLAs
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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 Machine Learning
Sep 17
Not All Layers Need Tuning: Diagnosing and Directing Adaptation in Vision-Language-Action Models
arXiv:2609.18084v1 Announce Type: cross Abstract: Fine-tuning a Vision-Language-Action (VLA) model for a new deployment environment is expensive, yet most methods apply uniform-capacity adapters to e...
arXiv Computer Vision
Sep 3
Towards Zero-Shot Transfer Across Embodiments For Driving VLAs
The paper investigates how Vision‑Language‑Action (VLA) models can generalise across different driving environments and camera setups. It introduces a multi‑dataset training strategy and an auxiliary objective called BEV‑Forcing, which injects bird‑eye‑view spatial information into the VLA backbone to improve both in‑distribution and out‑of‑distribution performance on a limited number of camera rigs. The authors observe that while BEV‑Forcing helps when training data is scarce, its advantage diminishes as the number of training embodiments grows, suggesting that scaling diversity may reduce the impact of such auxiliary tasks.
arXiv Computer Vision
Sep 7
Cross-Domain Tracker Adaptation Without Target-Domain Labels via Vision-Language Agents
The paper introduces a Vision‑Language Model (VLM) that acts as a diagnostic agent to adapt a detect‑to‑track system to new domains without target‑domain labels. By inspecting rendered tracking outputs, the VLM identifies failure modes and iteratively recommends parameter updates, recovering a significant portion of performance lost when transferring hyperparameters from a source domain. Experiments on MOT17→MOT20 show the VLM tuner restores 67.8% of the lost headroom, while Bayesian optimization with proxy objectives performs poorly under large domain shifts.
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
Oct 16
EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training
EmbodiedMidtrain bridges the gap between Vision‑Language Models (VLMs) and Vision‑Language‑Action Models (VLAs) by first analyzing the distribution mismatch between their data. It introduces a lightweight proximity estimator that selects VLA‑aligned samples from a large VLM pool, creating the EmbodiedMidtrain‑1M dataset used to mid‑train VLMs before VLA fine‑tuning. Experiments on simulated and real‑world robot tasks show consistent performance gains across different VLM backbones and VLA action heads, with early training steps already benefiting from the improved initialization.