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arXiv AI By Shijing Hu, Xuancheng Ren, Zhihui Lu, Pan Zhou

Where Draft Trees Lose Target Mass: Exit-Guided Speculative Decoding

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The paper introduces Tree Exit Verification (TEV), an exact, level‑parallel speculative decoding method that uses a single exit‑node decision and a bonus‑token decision to verify draft continuations. By deriving a canonical exit law, the authors show that TEV achieves the same acceptance as optimal verifiers while reducing verifier‑stage latency by 15%. They also propose Exit‑Guided Draft‑Tree Training (ExitTrain), which improves draft trees and increases average output‑block length by 13%, leading to a 14% end‑to‑end speedup over DDTree across dialogue, code, and mathematical reasoning tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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