Don't Blind Your VLA: Aligning Visual Representations for OOD
  Generalization
The growing success of Vision-Language-Action (VLA) models stems from the
promise that pretrained Vision-Language Models (VLMs) can endow agents with
transferable world knowledge and vision-language (VL) grounding, laying a
foundation for action models with broader generalization. Yet when these VLMs
are adapted to the action modality, it remains unclear to what extent their
original VL representations and knowledge are preserved. In this work, we
conduct a systematic study of representation retention during VLA fine-tuning,
showing that naive action fine-tuning leads to degradation of visual
representations. To characterize and measure these effects, we probe VLA's
hidden representations and analyze attention maps, further, we design a set of
targeted tasks and methods that contrast VLA models with their counterpart
VLMs, isolating changes in VL capabilities induced by action fine-tuning. We
further evaluate a range of strategies for aligning visual representations and
introduce a simple yet effective method that mitigates degradation and yields
improved generalization to out-of-distribution (OOD) scenarios. Taken together,
our analysis clarifies the trade-off between action fine-tuning and the
degradation of VL representations and highlights practical approaches to
recover inherited VL capabilities. Code is publicly available:
https://blind-vla-paper.github.io
690b2b3ad70e173c8452913b
Nikita Kachaev
claimed_verified
2025-11-06T11:48:27.072Z
64705ef84be5cf1f3348e283
https://cdn-avatars.huggingface.co/v1/production/uploads/64705ef84be5cf1f3348e283/mE8jsFgEk7AN_8KiH8NNW.jpeg
Nikita
tttonyalpha
user
tttonyalpha
690b2b3ad70e173c8452913c
Mikhail Kolosov
claimed_verified
2025-12-15T10:27:54.822Z
6746007fec397570749d9f8e
https://cdn-avatars.huggingface.co/v1/production/uploads/6746007fec397570749d9f8e/EV3W2emcC1Yb2JQAnmWMs.jpeg
Mikhail Kolosov
idkolosofff
user
idkolosofff
690b2b3ad70e173c8452913d
Daniil Zelezetsky
claimed_verified
2025-11-05T16:24:10.935Z
67583d59b75044e5c970876e
/avatars/85d5797197bc89775922fccd6c4db3ce.svg
Daniil Zelezetsky
zelezetsky
user
zelezetsky
690b2b3ad70e173c8452913e
Alexey K. Kovalev
690b2b3ad70e173c8452913f
Aleksandr I. Panov
Don't Blind Your VLA: Aligning Visual Representations for OOD
  Generalization
The growing success of Vision-Language-Action (VLA) models stems from the
promise that pretrained Vision-Language Models (VLMs) can endow agents with
transferable world knowledge and vision-language (VL) grounding, laying a
foundation for action models with broader generalization. Yet when these VLMs
are adapted to the action modality, it remains unclear to what extent their
original VL representations and knowledge are preserved. In this work, we
conduct a systematic study of representation retention during VLA fine-tuning,
showing that naive action fine-tuning leads to degradation of visual
representations. To characterize and measure these effects, we probe VLA's
hidden representations and analyze attention maps, further, we design a set of
targeted tasks and methods that contrast VLA models with their counterpart
VLMs, isolating changes in VL capabilities induced by action fine-tuning. We
further evaluate a range of strategies for aligning visual representations and
introduce a simple yet effective method that mitigates degradation and yields
improved generalization to out-of-distribution (OOD) scenarios. Taken together,
our analysis clarifies the trade-off between action fine-tuning and the
degradation of VL representations and highlights practical approaches to
recover inherited VL capabilities. Code is publicly available:
https://blind-vla-paper.github.io
Systematic study reveals that naive action fine-tuning degrades visual representations in Vision-Language-Action models, but targeted strategies can mitigate this and improve generalization.
Vision-Language-Action models Vision-Language Models VLA fine-tuning visual representations attention maps out-of-distribution scenarios