TriSpec: Ternary Speculative Decoding via Lightweight Proxy Verification

• URL: http://arxiv.org/abs/2601.23180v1
• Date: Fri, 30 Jan 2026 17:04:18 GMT
• Title: TriSpec: Ternary Speculative Decoding via Lightweight Proxy Verification
• Authors: Haoyun Jiang, Junqi He, Feng Hong, Xinlong Yang, Jianwei Zhang, Zheng Li, Zhengyang Zhuge, Zhiyong Chen, Bo Han, Junyang Lin, Jiangchao Yao,
• Abstract summary: Speculative decoding offers significant speed-ups through its lightweight drafting and parallel verification mechanism.<n>We propose TriSpec, a novel ternary SD framework that introduces a lightweight proxy to significantly reduce computational cost.<n>Experiments on the Qwen3 and DeepSeek-R1-Distill-Qwen/LLaMA families show that TriSpec achieves up to 35% speedup over standard SD.
• Score: 63.65902785448346
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inference efficiency in Large Language Models (LLMs) is fundamentally limited by their serial, autoregressive generation, especially as reasoning becomes a key capability and response sequences grow longer. Speculative decoding (SD) offers a powerful solution, providing significant speed-ups through its lightweight drafting and parallel verification mechanism. While existing work has nearly saturated improvements in draft effectiveness and efficiency, this paper advances SD from a new yet critical perspective: the verification cost. We propose TriSpec, a novel ternary SD framework that, at its core, introduces a lightweight proxy to significantly reduce computational cost by approving easily verifiable draft sequences and engaging the full target model only when encountering uncertain tokens. 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/LLaMA families show that TriSpec achieves up to 35\% speedup over standard SD, with up to 50\% fewer target model invocations while maintaining comparable accuracy.

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