Step-Wise Refusal Dynamics in Autoregressive and Diffusion Language Models

📰 ArXiv cs.AI

Learn how step-wise refusal dynamics impact autoregressive and diffusion language models, and how to analyze their behavior

advanced Published 8 Jun 2026

Action Steps

Analyze the sampling mechanisms in autoregressive and diffusion language models to understand their impact on refusal behavior
Implement step-wise refusal dynamics in a language model to observe its effects on generation quality
Compare the refusal behavior of autoregressive and diffusion models using metrics such as perplexity and accuracy
Apply the findings to improve the robustness of language models against jailbreak attacks
Evaluate the trade-offs between parallel decoding and refusal behavior in diffusion language models

Who Needs to Know This

NLP researchers and engineers working with language models can benefit from understanding step-wise refusal dynamics to improve model performance and robustness

Key Insight

💡 Step-wise refusal dynamics play a crucial role in shaping the behavior of autoregressive and diffusion language models, and understanding them can help improve model performance and robustness

Key Takeaways

Learn how step-wise refusal dynamics impact autoregressive and diffusion language models, and how to analyze their behavior

Full Article

Title: Step-Wise Refusal Dynamics in Autoregressive and Diffusion Language Models

Abstract:
arXiv:2602.02600v3 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 diff

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