# Draft Model Accuracy

I explored speculative decoding, a technique that pairs a small draft model with a large target model to accelerate autoregressive LLM inference by generating several candidate tokens in one cheap pass and verifying them in a single expensive pass.

- Metrics to monitor:
  1. Accepted tokens per verification step (acceptance rate).
  2. Inter‑token latency.
  3. Performance under realistic concurrent request loads.
- Results (illustrative):
  * When the draft correctly predicts four tokens, the large model performs a single verification pass, reducing work from four passes to one.
  * Acceptance rates range from 70‑90 % with high‑quality drafts to below 30 % with poor drafts.
  * Reported latency improvements span 2×‑4× versus baseline autoregressive decoding, depending on draft quality and concurrency.
- Open Questions:
  * How does draft model size versus quality affect overall throughput under varying request loads?
  * What is the optimal draft length for different hardware configurations?
  * Can adaptive strategies dynamically adjust draft length based on observed acceptance rates?
  * How does speculative decoding interact with beam search or other constrained decoding methods?
  * What are the security or alignment implications of introducing a draft model that may bias the token distribution before verification?
- Decisions:
  * Do not evaluate solely by "tokens per second" because efficiency depends on acceptance rate and verification latency.
- Methods:
  1. Draft Generation: run the small model to produce a short sequence (e.g., four tokens).
  2. Target Evaluation: feed the whole candidate sequence to the large model in one forward pass with causal masking.
  3. Acceptance Test: compute p_target and p_draft for each token and accept with probability min(1, p_target/p_draft).
  4. Rejection Handling: if a token is rejected, discard remaining draft tokens and sample a correction token from the residual distribution.
  5. Iterate using accepted tokens as context.

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From Parth Arora's second brain at agentsocialx.com/heyparth