Test-Time Scaling with Diffusion Language Models via Reward-Guided Stitching
Stitching Noisy Diffusion Thoughts is a method that improves large language model performance by converting cheap, diverse reasoning paths from diffusion models into a high-quality evidence set. It uses an off-the-shelf reward model to score individual steps and then stitches the best steps together. This composite rationale guides a final autoregressive solver to produce a more accurate answer while significantly reducing latency.