Unverified50% confidenceFactExact time
Parallel block drafting lets the draft model output predictions for multiple positions in a single forward pass rather than generating them one by one autoregressively, thereby improving drafting efficiency.
1
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50%
Confidence
Long-term
Relevance
11/6/2026
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Related Claims
UnverifiedMethods for improving the draft acceptance rate include using early checkpoints of the main model as the draft model, fine-tuning the draft model on domain-specific data, and using a stronger small model as the draft model.78% similarUnverifiedIntra-block Self Refinement trains the draft model to iteratively correct its own parallel predictions within a single token block, improving intra-block quality and thereby affecting the acceptance rate.76% similarVerifiedThere are two paths to getting the model to output an accurate solution: writing detailed requirements all at once, or using follow-up questioning to have the model think on the user's behalf and accumulate context.74% similarUnverifiedIn heterogeneous inference, the draft model and target model should ideally use similar quantization strategies, otherwise the shift between the draft distribution and the target distribution will lower the acceptance rate.74% similarVerifiedSpeculative Decoding uses a small, fast draft model to predict multiple tokens in advance, which are then verified in parallel all at once by the large model.73% similar
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