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DCOCO restores context across parallel speculative drafts

huggingface.co by Roy Eisenstadt et al. Editor score 8/10

DCOCO keeps multiple draft-token candidates and checks their compatibility across positions in parallel, restoring context among draft tokens at low additional cost. Its throughput results across four verifier backbones and ten benchmarks make it relevant to speculative-decoding design.

What: DCOCO is a lightweight correction mechanism for parallel speculative drafts in large language model inference.
Method: It retains multiple token candidates and selects contextually compatible tokens across adjacent draft positions before target-model verification.
Why it matters: Restoring draft context while preserving parallelism improves throughput; DCOCO led evaluated configurations and reached up to 6.6× speedup over vanilla decoding.

DCOCO corrects parallel draft candidates before the target model verifies them. Sketched from the article's own text.

Read on huggingface.co → Code · 22 stars

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