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arXiv · 2610.40271

Gaussian Distillation of Flow Policies

Youssef Saied·Ziyad Sheebaelhamd·Claire Vernade·François Fleuret
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Abstract

Flow policies are among the strongest robot controllers and are well suited to modelling multimodal action distributions. Our analysis shows that the continuous-action distributions of the studied flow policies are well approximated by Gaussians. We propose the Gaussian Action Readout (GAR), which replaces a flow policy's final layer with a Gaussian readout trained on just 0.02% of eligible states, while keeping the rest of the network frozen. It samples with one action-network pass, gives closed-form likelihoods, and achieves near-teacher task success at up to twice the inference speed.

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Youssef Saied, Ziyad Sheebaelhamd, Claire Vernade, François Fleuret. 2026-10-29. Gaussian Distillation of Flow Policies. https://arxiv.org/abs/2610.40271

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