What happens when a pretrained generative robot policy is provided a constant initial noise as input, rather than repeatedly sampling it from a Gaussian? We demonstrate that the performance of a pretrained, frozen diffusion or flow matching policy can be improved with respect to a downstream reward by swapping the sampling of initial noise from the prior distribution (typically isotropic Gaussian) with a well-chosen, constant initial noise input - a golden ticket. We propose simple search methods to find golden tickets using Monte-Carlo policy evaluation that keeps the pretrained policy frozen, does not train any new networks, and is applicable to all diffusion/flow matching policies (and therefore many VLAs). Our approach to policy improvement makes no assumptions beyond being able to inject initial noise into the policy and calculate (sparse) task rewards of episode rollouts, making it deployable with no additional infrastructure or models. Our method improves the performance of policies in 46 out of 51 tasks across simulated and real-world robot manipulation benchmarks, with absolute improvements in success rate by up to 55% for some simulated tasks, and 28% within 60 search episodes for real-world tasks. Our approach naturally extends to multi-task settings, where we improve the average performance of a VLA policy across 7 tasks by 14% using a single noise vector. Further, we find that, a golden ticket optimized for one task can also boost performance in other related tasks for the same VLA policy. We release a codebase with pretrained policies and golden tickets for simulation benchmarks using VLAs, diffusion policies, and flow matching policies.

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