Papers by Zhaoyu Ma
Saber: Efficient Sampling with Adaptive Acceleration and Backtracking Enhanced Remasking for Diffusion Language Model in Code Generation (2026.acl-long)
Copied to clipboard
| Challenge: | Diffusion language models (DLMs) offer advantages in parallel generation and bidirectional context modeling, but they face a critical trade-off between inference speed and output quality for tasks with strict structural constraints such as code generation. |
| Approach: | They propose an efficient sampling algorithm that reduces the number of tokens unmasked per step based on the model’s evolving confidence. |
| Outcome: | The proposed method improves Pass@1 accuracy by 1.9% while achieving 251.4% inference speedup. |