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VLA models show varied sample efficiency in fine-tuning, study finds

By PulseAugur Editorial · [1 sources] ·

A new research paper investigates the sample efficiency of Vision-Language Action (VLA) models when fine-tuned on demonstrations. The study reveals significant differences in adaptation efficiency among six VLA models, including $\pi_0$, $\pi_{0.5}$, and GR00T N1-N1.7, across four LIBERO benchmark suites. Notably, $\pi_{0.5}$ demonstrated superior sample efficiency, requiring fewer demonstrations than $\pi_0$ and achieving higher success rates with limited data. The research also found that within the GR00T models, N1.5 was more efficient than N1.6 and N1.7, despite the latter's stronger zero-shot performance. AI

IMPACT Highlights that model architecture and pretraining choices significantly impact fine-tuning efficiency, guiding future VLA development.

RANK_REASON Research paper detailing findings on VLA model sample efficiency. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

VLA models show varied sample efficiency in fine-tuning, study finds

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Research paper detailing findings on VLA model sample efficiency. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Rahul Chand, Miguel Villa Floran ·

    How Many Demonstrations Does a VLA Need? On the Sample Efficiency of VLA Finetuning

    arXiv:2611.09964v2 Announce Type: replace Abstract: Pretrained vision language action (VLA) models show strong zero-shot performance but they are rarely deployed directly and are often first post-trained on demonstrations from the target setting. Yet VLAs are typically compared b…

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