Optimizing Denoising Trajectories in dLLMs: A Lightweight Evolutionary Heuristic Approach
Diffusion Large Language Models (dLLMs) have recently emerged as a promising alternative to conventional Auto-Regressive (AR) Large Language Models (LLMs). By leveraging bidirectional attention and parallel decoding, dLLMs enable more efficient generation. However, they require a carefully designed denoising scheduler at inference time (absent during training) whose choice significantly impacts generation quality. While confidence-based heuristic schedulers have shown strong empirical performance, they suffer from two critical failure modes: EOS Overflow and Proximal Bias. Through in-depth analysis of the Transformer's attention patterns, we reveal that these failures stem from certain po...
| Authors | Zijian Zhao, Dian Jin, Xialiang Tong et al. |
|---|---|
| Primary category | cs.CL |
| Published | 15 July 2026 |
| Last updated at source | 15 July 2026 |
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