arXiv AI August 25, 2026 By Jewon Yeom, Jaewon Sok, Seonghyeon Park, Jeongjae Park, Hwiyeong Lee, Taesup Kim

Answer First, Reason Later: When Commitment Order Costs Accuracy in Diffusion Language Models

The paper studies how the order in which tokens are committed in masked diffusion language models affects accuracy. It finds that when the final answer is committed before the preceding reasoning (an answer‑first trajectory), accuracy can suffer compared to unrestricted decoding, especially on tasks like GSM8K and MATH‑500. Experiments with controlled token positions show that delaying the answer token can improve performance, indicating that commitment order influences the context and output allocation of the model.

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Answer First, Reason Later: Commitment Order in Diffusion LLMs

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