Pivot-SD: Efficient Self-Distillation for Masked Diffusion Language Models
Masked diffusion language models (dLMs) offer a promising parallel alternative to autoregressive models for complex reasoning. However, they face a distinct credit-assignment challenge, since a few commitments during denoising sharply reduce the uncertainty over the remaining masked positions and shape much of the response. Most post-training recipes for dLMs do not use this si
Reading in Plain English mode — toggle below to switch
Masked diffusion language models (dLMs) offer a promising parallel alternative to autoregressive models for complex reasoning. However, they face a distinct credit-assignment challenge, since a few commitments during denoising sharply reduce the uncertainty over the remaining masked positions and shape much of the response. Most post-training recipes for dLMs do not use this si
This paper introduces an efficient self-distillation method for masked diffusion language models, which are a promising alternative to autoregressive models for complex reasoning. By focusing on high-impact commitments, it addresses a key challenge in training these models, potentially leading to more efficient and effective dLMs.