Target
Increase local decode throughput after model quality is accepted.
Failure this recipe addresses
Autoregressive decode latency is the measured user-visible bottleneck.
Data contract
Target-domain sequences for head training plus a frozen acceptance and quality suite.
Method or policy
Train draft heads or a draft model, verify proposed tokens with the target model, and measure accepted tokens per unit work.
Evaluation contract
End-to-end tok/s at output parity.
- acceptance rate
- quality parity
- memory
- training cost
- short prompts
Budget and stop rule
Medusa/EAGLE smoke retained; a production recipe is a new training experiment.
Stop if acceptance cannot repay verification and memory overhead.
Decision rule
Adopt only on an end-to-end decode benchmark, not head acceptance alone.
Learning exercise
Compute expected speedup from draft length, acceptance rate, and verification cost.
Identify when speculative decoding becomes slower than ordinary decoding.
How to read this dossier
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Status and disposition describe what PostTrainLLM retained when this record was indexed. They are not a live product promise, a newly run benchmark, or permission to restart historical work. Unknown or unmeasured fields remain unknown rather than being treated as zero.
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Source provenance
The normalized record comes from docs/recipes/registry.json. The links below are the tracked evidence and explanatory sources preserved with the record.