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DEdit is introduced, a diffusion-based drafter that can not only draft by conventional parallel unmasking but also iteratively edit its draft through token-to-token predictions, and ProposalMix, a training scheme that mixes draft predictions with ground-truth tokens based on first-pass confidence during training.
It is shown that a simple two-stage scheme, OPD-then-RL, consistently outperforms pure OPD, pure RLVR, and all such joint baselines across logic and math reasoning benchmarks.
This work studies whether smaller language models can serve as efficient and reliable rubric-based judges, and compares three ways of extracting criterion-level judgments from small models: Generative verdicts, Yes/No Logprob margins, and Probe judges.
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