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Diffusion Language Models Know the Answer Before Decoding
Created by
Haebom
Author
Pengxiang Li, Yefan Zhou, Dilxat Muhtar, Lu Yin, Shilin Yan, Li Shen, Yi Liang, Soroush Vosoughi, Shiwei Liu
Outline
To improve the inference speed of diffusion language models (DLMs), we propose a training-free, fast decoding paradigm called "Prophet," which leverages the phenomenon of early-answer convergence. Prophet leverages the DLM's ability to identify the correct answer with only half the final decoding steps. This approach improves inference speed by stopping decoding in the middle of the process and decoding the remaining tokens at once. Experiments on various tasks using the LLaDA-8B and Dream-7B models demonstrate that Prophet reduces decoding steps by up to 3.4x while maintaining high generation quality.
Takeaways, Limitations
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Takeaways:
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A novel approach to speed up DLM inference: leveraging the early answer convergence phenomenon to improve inference speed.
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Can be integrated into existing DLMs without additional training and implemented with low overhead
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Performance validation across multiple models and tasks, including the LLaDA-8B and Dream-7B models.
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Redefining DLM inference as the problem of deciding when to stop sampling
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Limitations:
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Potential performance deviations for specific DLM models and tasks
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Further research is needed to determine the generalizability of the proposed method.
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Analysis of performance changes based on model size, dataset type, etc. is required.
