VISTA: value-informed schedule trimming for sparse SMC guidance in discrete diffusion (arXiv:2610.20669v1)
The paper introduces VISTA, a training-free method that selects a subset of timesteps to apply Sequential Monte Carlo (SMC) guidance in reward-guided discrete diffusion under a fixed compute budget. VISTA uses value statistics from a small number of full-guidance warmup generations to derive and optimize an empirical surrogate (via dynamic programming), yielding task-dependent sparse schedules that often match or approach full-guidance quality while substantially reducing per-generation latency (e.g., toxic-text: unique-toxic ratio 0.99 vs 1.00 and latency reduced from 204.23 s to 38.16 s, a 5.35× speedup).