Steering Without Breaking: Mechanistically Informed Interventions for Discrete Diffusion Language Models

This paper investigates the limitations of uniform interventions in discrete diffusion language models (DLMs), demonstrating they degrade controlled generation quality. The authors find that different attributes commit at distinct stages of the denoising process, proposing an adaptive scheduler to concentrate interventions efficiently.