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[2508.19982] Diffusion Language Models Know the Answer Before Decoding
Summary
The paper discusses Diffusion Language Models (DLMs) and introduces a new decoding method called Prophet, which allows for faster inference by identifying correct answers earlier in the decoding process.
Why It Matters
This research addresses the inefficiencies in DLMs by proposing a method that significantly reduces decoding time without sacrificing output quality. As DLMs gain traction in AI applications, optimizing their performance is crucial for real-world deployment.
Key Takeaways
- Diffusion Language Models can identify correct answers before full decoding.
- The Prophet method reduces decoding steps by up to 3.4x.
- Early answer convergence can enhance DLM efficiency significantly.
- Prophet requires no additional training and integrates easily into existing systems.
- Empirical results show high generation quality with reduced inference time.
Computer Science > Computation and Language arXiv:2508.19982 (cs) [Submitted on 27 Aug 2025 (v1), last revised 25 Feb 2026 (this version, v4)] Title:Diffusion Language Models Know the Answer Before Decoding Authors:Pengxiang Li, Yefan Zhou, Dilxat Muhtar, Lu Yin, Shilin Yan, Li Shen, Soroush Vosoughi, Shiwei Liu View a PDF of the paper titled Diffusion Language Models Know the Answer Before Decoding, by Pengxiang Li and 7 other authors View PDF HTML (experimental) Abstract:Diffusion language models (DLMs) have recently emerged as an alternative to autoregressive approaches, offering parallel sequence generation and flexible token orders. However, their inference remains slower than that of autoregressive models, primarily due to the cost of bidirectional attention and the large number of refinement steps required for high quality outputs. In this work, we highlight and leverage an overlooked property of DLMs early answer convergence: in many cases, the correct answer can be internally identified by half steps before the final decoding step, both under semi-autoregressive and random remasking schedules. For example, on GSM8K and MMLU, up to 97% and 99% of instances, respectively, can be decoded correctly using only half of the refinement steps. Building on this observation, we introduce Prophet, a training-free fast decoding paradigm that enables early commit decoding. Specifically, Prophet dynamically decides whether to continue refinement or to go "all-in" (i.e., decode a...
