Towards Unbiased On-Policy Distillation for Block Diffusion Language Models
On-policy distillation (OPD) has emerged as an effective post-training paradigm for language models, with recent efforts extending it to block diffusion language models (BDLMs). However, existing studies focus almost exclusively on small block sizes, leaving distillation into student models with larger blocks underexplored. In this work, we investigate this regime and reveal two critical optimization biases that induce severe training instability. First, mismatched block boundaries between teacher and student cause \textbf{\textit{context misalignment}}, providing distorted supervisory signals that misguide student decoding. Second, even under aligned contexts, an \textbf{\textit{intrinsi...
| Authors | Zaiquan Yang, Fei Wei, Yong Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 4 October 2026 |
| Last updated at source | 15 October 2026 |
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