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Value-Informed Schedule Trimming for Accelerated Sequential Monte Carlo Guidance in Discrete Diffusion
Author 1, Author 2, Author 3, Author 4, Author 5
Sequential Monte Carlosparse guidanceoptimizationdiffusion models
2610.20669
Builder Relevance
1h ago80%
Abstract
VISTA is a training-free method that optimizes guided timesteps for Sequential Monte Carlo guidance, achieving significant speedup while maintaining performance across various tasks.
Reality Card
Core Claim
VISTA outperforms hand-designed sparse schedules and approaches full-guidance performance while achieving a 5.35x speedup in post-selection per-generation latency.
Method / Result
Achieved a reduction in post-selection per-generation latency from 204.23 s to 38.16 s.
Limitations
The method relies on value statistics from a small number of warmup generations, which may affect generalizability.
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