arXiv Machine Learning By Ali Asaria, Tony Salomone, Deep Gandhi

Neither Parallel Nor Sequential: How DiffusionGemma Actually Commits Tokens

Read the original on arXiv Machine Learning →

arXiv:2606. 14620v1 Announce Type: new Abstract: Open diffusion language models are marketed as parallel, non-autoregressive decoders, yet the order in which a shipped checkpoint actually commits its tokens is almost never measured.

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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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