Optimizing Ratio Tempering Factors

Background: Speculative decoding for diffusion models integrates autoregressive verification with parallel denoising steps, creating a hybrid generation trajectory.

Question / Future Work: Analyze the theoretical implications and practical performance of altering the rejection-sampling acceptance probability by using ratio tempering factors ($\gamma \neq 1$) to intentionally bias the acceptance/rejection decision, potentially optimizing the accuracy/speed trade-off beyond the standard exact ratio ($\gamma=1$).