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Publikation

Finetuning Vision-Language-Action Models Requires Fewer Layers Than You Think

Gia-Binh Nguyen; Trong-Bao Ho; Thien-Loc Ha; Khoa Vo; Philip Lund Møller; Quang T. Nguyen; Long Dinh; Tung M. Luu; Tuan Quang Dam; Vu Duong; Trung Le; Nghi D. Q. Bui; Minh Vu; Tran Nguyen Le; An Thai Le; Hong Anh Le; Daniel Sonntag; James Zou; Jan Peters; Duy M. H. Nguyen; Ngo Anh Vien
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2606.20246, Pages 1-16, arXiv, 2026.

Zusammenfassung

Vision-Language-Action (VLA) models pre-trained on massive video- robot datasets have revolutionized robotic manipulation, yet their multi-billion pa- rameter architectures impose prohibitive computational burdens during downstream fine-tuning and real-time inference. In this work, we reveal a highly non-trivial architectural characteristic of these continuous control foundation policies (e.g., π0, GR00T-N1.5): despite being trained on diverse physical trajectories, they exhibit severe layer-wise representational redundancy. To exploit this, we introduce a structural compression pipeline that is entirely training-free, bypassing the need of existing methods to load full-scale models to learn optimized token reductions or dynamic layer selectors. Instead, using only a single forward pass via Centered Kernel Alignment to identify redundant layer features, we remove twin layers to permanently compress the model depth by up to 50% across both the VLM backbone and the continuous control policy head. Downstream fine-tuning of this streamlined architecture yields a dual acceleration benefit: a 40–50% reduction in training time and up to 30% faster real-time inference, while matching or exceed- ing full-scale base model performance. We comprehensively validate our method across three simulation benchmarks (LIBERO, RoboCasa, SimplerEnv) and 10 diverse real-world manipulation tasks across 4 unique robotic embodiments. These results prove that advanced VLAs require significantly fewer layers than previously assumed, offering a highly compute-efficient paradigm for scalable robot learning.

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