You've Got a Golden Ticket: Improving Generative Robot Policies With A Single Noise Vector

Generative robot policies trained on demonstrations using behavior cloning often learn actions that are sub-optimal or misaligned with respect to the downstream task. Policy improvement approaches aim to bridge this gap and improve the cumulative reward with minimal interventions on the pre-trained policy. However, an impediment to their practical deployment is the requirement of cumbersome hyperparameter tuning specific to model or task families. In this work, we present an episodic, derivative-free latent policy improvement approach that works with little to no changes across model scales from MLPs to VLAs. 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 show the prevalence of golden tickets by improving the policy performance of 46 out of 51 tasks across manipulation benchmarks, with absolute improvements in success rate by up to 79% for simulated tasks, and 28% within 60 search episodes for real-world tasks. Further, we find that the versatility of our approach opens up new avenues such as simultaneous policy improvement for multiple downstream rewards. Project webpage: https://lottery-tickets.rai-inst.com/.