# ProVe: Proposer-Verifier Decoding

**Authors:** Ziqi Jin, Weiwen Xu, Jiaxi Li, Yujie Zhong, Qi She, Aixin Sun  
**Lab:** NTU Singapore · **Published:** 19 Oct 2026 · **Upvotes:** 4  
**Links:** [arXiv](https://arxiv.org/abs/2610.27815) · [PDF](https://arxiv.org/pdf/2610.27815) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2610.27815.html)

## Key numbers

- **2.5×**: ProVe-lossless reaches 2.5x average AR throughput without training a separate draft head
- **2.7×**: ProVe reaches 2.7x average throughput at 2.6 accuracy points below AR on the six-task average

## Abstract

Speculative decoding accelerates autoregressive (AR) language models by letting a small model draft tokens serially and a large model verify them in parallel. We reverse these roles. Starting from a standard AR checkpoint, we fine-tune the large model into a proposer that predicts a block of future tokens in a single forward pass, then use one additional forward pass to verify the proposed block. This gives ProVe, Proposer-Verifier decoding. The verifier can be a smaller AR model or the proposer itself; the latter gives ProVe-lossless, which reproduces the proposer's greedy AR decoding. Block proposals already provide substantial room for acceleration: with a block size of 16, the proposed block has an average AR-matching prefix of 6.6 tokens on GSM8K. Across six benchmarks with a 32B proposer, ProVe-lossless reaches 2.5x average AR throughput without training a separate draft head. With a small 3B verifier, ProVe reaches 2.7x average throughput at 2.6 accuracy points below AR on the six-task average, providing a controllable tradeoff between speed and accuracy.

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Notes written by AI from the paper, numbers copied from the abstract. Check the paper before you rely on them. From AI Papers Daily.