AI RESEARCH
FASTER: Value-Guided Sampling for Fast RL
arXiv CS.AI
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ArXi:2604.19730v1 Announce Type: cross Some of the most performant reinforcement learning algorithms today can be prohibitively expensive as they use test-time scaling methods such as sampling multiple action candidates and selecting the best one. In this work, we propose FASTER, a method for getting the benefits of sampling-based test-time scaling of diffusion-based policies without the computational cost by tracing the performance gain of action samples back to earlier in the denoising process.