Bayesian Entropy-based Reordering for Calibrated Diffusion Language Models
Masked Diffusion Language Models (MDLMs) generate sequences by iteratively replacing masked tokens with model predictions. At each denoising step, the decoder chooses which positions are sufficiently confident to commit. Existing decoding methods typically rely on softmax confidence, which can be miscalibrated. We introduce BayesER (BAYESian Entropy-based Reordering), a post-hoc Bayesian decoding framework that uses predictive uncertainty to guide token commitment. In BayesER, we construct a lightweight approximate posterior centered at the pretrained checkpoint, similar to Laplace-LoRA but without training LoRA adapters. We average predictions over posterior samples and use predictive en...
| Authors | Zhejun Jiang, Mijung Park |
|---|---|
| Primary category | cs.LG |
| Published | 4 October 2026 |
| Last updated at source | 4 October 2026 |
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