Step-Wise Refusal Dynamics in Autoregressive and Diffusion Language Models
Quick summary
arXiv:2602.02600v4 Announce Type: replace-cross Abstract: Diffusion language models (DLMs) have recently emerged as a competitive alternative to autoregressive (AR) models, offering parallel decoding, competitive generation quality, and initial evidence of improved jailbreak robustness. Despite this progress, the role of sampling mechanisms in shaping refusal behavior remains poorly understood. To address this gap, we present a comprehensive study of step-wise refusal dynamics. We show that diffusion remasking can promote recovery from harmful intermediate generations, provide evidence that th
Key takeaways
- arXiv:2602.02600v4 Announce Type: replace-cross Abstract: Diffusion language models (DLMs) have recently emerged as a competitive alternative to autoregressive (AR) models, offering parallel decoding, competitive generation quality, and initial evidence of improved jailbreak robustness.
- Despite this progress, the role of sampling mechanisms in shaping refusal behavior remains poorly understood.
- To address this gap, we present a comprehensive study of step-wise refusal dynamics.
Why it matters
“Step-Wise Refusal Dynamics in Autoregressive and Diffusion Language Models” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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