Title: TriSpec: Ternary Speculative Decoding via Lightweight Proxy Verification
Primary area: foundation or frontier models, including LLMs
Keywords: LLM Acceleration, Speculative Decoding, Proxy verifier

Abstract: The enhanced reasoning capabilities of Large Language Models (LLMs) have led to longer response sequences, yet the inference efficiency is fundamentally limited by their serial, autoregressive generation. Speculative decoding (SD) offers a powerful solution, providing significant speed-ups through its lightweight drafting and parallel verification mechanism. While existing work has made considerable progress in improving the draft model's generation efficiency and alignment, this paper boosts SD from a new angle: the verification cost. We propose TriSpec, a novel ternary SD framework with proxy verification. At its core, TriSpec introduces a lightweight proxy model that handles the initial verification. This proxy significantly reduces computational cost by approving easily verifiable draft sequences and only engages the full target model when encountering uncertain tokens, thus striking an optimal balance between efficiency and quality. TriSpec can be integrated with state-of-the-art SD methods like EAGLE-3 to further reduce verification costs, achieving greater acceleration. Extensive experiments on the Qwen3 and DeepSeek-R1-Distill-Qwen families show that TriSpec achieves up to 30\% speedup over standard SD, with up to 50\% fewer target model invocations while maintaining comparable accuracy.

TL;DR: We propose a ternary speculative decoding method that employs a proxy verifier to assist the target model in verification stage, thereby further accelerating standard speculative decoding.