Generalizable VLA Finetuning via Representation Anchoring and Language-Action Alignment
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, 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 L...
| Authors | Dwip Dalal, Shivansh Patel, Chahit Jain et al. |
|---|---|
| Primary category | cs.RO |
| Published | 15 July 2026 |
| Last updated at source | 28 September 2026 |
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