Saber: An Efficient Sampling with Adaptive Acceleration and Backtracking Enhanced Remasking for Diffusion Language Model
Summary: arXiv:2510.18165v2 Announce Type: replace
Abstract
Diffusion language models (DLMs) are emerging as a powerful and promising alternative to the dominant autoregressive paradigm, offering inherent advantages in parallel generation and bidirectional context modeling. However, the performance of DLMs on code generation tasks, which have stronger structural constraints, is significantly hampered by the critical trade-off between inference speed and output quality. We observed that accelerating the code generation process by reducing the number of sampling steps usually leads to a catastrophic collapse in performance.
Introduction
In recent years, the field of natural language processing has witnessed the rise of diffusion language models (DLMs) as a novel approach to generating text. Unlike traditional autoregressive models that generate text sequentially, DLMs allow for parallel generation, significantly improving efficiency. Despite these advantages, DLMs face challenges, particularly in tasks such as code generation, where the structured nature of the output requires high fidelity and accuracy.
The Challenge of Inference Speed and Output Quality
The primary challenge in utilizing DLMs for code generation lies in balancing inference speed with output quality. Reducing the number of sampling steps to accelerate the generation process often results in a decline in performance quality. This paper addresses this critical issue by introducing a new sampling method designed to optimize both speed and quality.
Introducing Saber
We present Saber, an innovative training-free sampling algorithm for DLMs aimed at improving inference speed and output quality in code generation tasks. Saber is built on two foundational insights regarding the DLM generation process:
- Adaptive Acceleration: The sampling process can be accelerated as the model gains more context about the code being generated, allowing for faster and more efficient predictions.
- Backtracking Mechanism: Incorporating a backtracking system enables the model to reverse generated tokens, thereby refining the output and mitigating potential errors in the generated code.
Experimental Results
To validate the effectiveness of Saber, we conducted extensive experiments on various mainstream code generation benchmarks. The results are promising:
- Saber achieved an average Pass@1 accuracy improvement of 1.9% compared to existing DLM sampling methods.
- Additionally, Saber realized an impressive average inference speedup of 251.4%, demonstrating its efficiency.
Conclusion
By capitalizing on the inherent advantages of diffusion language models, Saber significantly narrows the performance gap between DLMs and traditional autoregressive models in the realm of code generation. Our findings suggest that with further development and refinement, DLMs could become a leading choice for code generation tasks, combining speed with the quality essential for practical applications.
As the field evolves, we anticipate that innovations like Saber will play a crucial role in enhancing the capabilities of language models, potentially transforming the landscape of code generation and beyond.