Beyond Token Positions: Safety Alignment Across Denoising Steps in Diffusion Language Models

This paper investigates safety alignment in diffusion large language models (dLLMs), which generate text via iterative denoising instead of left‑to‑right decoding. By tracking token distributions and commitment decisions across denoising steps, the authors find that refusal signals are concentrated early in the denoising process and at leading response positions, and that early committed tokens strongly influence the final safety outcome. They introduce Refusal‑Aware Early Commitment (RAEC), a training‑free decoding method that preserves early refusal signals, and demonstrate that RAEC reduces attack success rates on LLaDA and Dream while largely maintaining utility.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• llms
• diffusion
• safety