| title | Generalizable VLA Finetuning via Representation Anchoring and Language-Action Alignment |
|---|---|
| authors | Dwip Dalal, Shivansh Patel, Chahit Jain, Jeonghwan Kim, Utkarsh Mishra, Alex Baratian, Hyeonjeong Ha, Heng Ji, Svetlana Lazebnik, Unnat Jain |
| date | 2026-07-15 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.13429 |
| upvotes | 14 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| primer | Vision-language-action models |
| abstract | Finetuning a pretrained vision-language model (VLM) on robot demonstrations via behavior cloning (BC) has become the standard recipe for vision-language-action (VLA) policies. However, BC finetuning progressively overwrites the pretrained representations that support visual and semantic generalization. Co-training on web image-text data, a common remedy, does not prevent this; it applies language and action losses to separate observations, leaving VLAs with language-action misalignment that standard manipulation benchmarks do not expose. We propose Anchor-Align, which augments BC with two objectives: Vision-Language Anchoring distills layer-wise representations from a frozen VLM copy to prevent this drift, while Language-Action Alignment converts each action target into a discrete motion-direction label and jointly trains language and action prediction on the same robot observation. On a physical xArm7 robot, across two widely used VLA architectures, Anchor-Align improves real-robot success on both (28% to 54% and 37% to 60%). At scale in simulation, we demonstrate consistent improvements on OOD perturbations, perceptual robustness, and long-horizon control across LIBERO-PRO, LIBERO-Plus, and CALVIN, respectively, suggesting that preserving pretrained representations and effective action learning are not fundamentally at odds. Project page: anchoralignvla.github.io |
cite
@misc{dalal2026generalizable,
title={Generalizable VLA Finetuning via Representation Anchoring and Language-Action Alignment},
author={Dwip Dalal and Shivansh Patel and Chahit Jain and Jeonghwan Kim and Utkarsh Mishra and Alex Baratian and Hyeonjeong Ha and Heng Ji and Svetlana Lazebnik and Unnat Jain},
year={2026},
eprint={2607.13429},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.13429}
}