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training-alignment

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

arXiv:2611.09964v1 Announce Type: new Abstract: Pretrained vision language action (VLA) models show strong zero-shot performance, but are rarely deployed directly and are often first post-trained on demonstrations from the target setting. Yet VLAs are typically compared by zero-shot or full-data fine-tuned success, revealing little about the data and compute required for adaptation. We study adaptation efficiency for six VLAs ($\pi_{0}$, $\pi_{0.5}$, and GR00T N1-N1.7) on four LIBERO suites, varying both demonstrations and training steps. This reveals differences hidden by full-data fine-tuning: models within two points on the full LIBERO-Spatial dataset differ by up to $3\times$ in success with 50 demonstrations. $\pi_{0.5}$ needs $2$--$5\times$ fewer demonstrations than $\pi_{0}$ and is the most sample-efficient model at every budget, with the largest gap on the long-horizon suite. Within GR00T, N1.5 adapts more efficiently than N1.6 and N1.7 on three of four suites, despite their stronger zero-shot performance. We study one pretraining factor behind these differences by freezing the VLM during finetuning: this severely degrades the $\pi$ models, whose VLM is jointly trained with the action expert during pretraining, but barely affects GR00T models. Finally, validation loss on held-out demonstrations tracks closed-loop success within a suite, but understates how much more sample-efficient $\pi_{0.5}$ is than $\pi_{0}$.

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arXiv cs.CV