TALON: Confidence-Aware Speculative Decoding with Adaptive Token Trees

• URL: http://arxiv.org/abs/2601.07353v1
• Date: Mon, 12 Jan 2026 09:26:45 GMT
• Title: TALON: Confidence-Aware Speculative Decoding with Adaptive Token Trees
• Authors: Tianyu Liu, Qitan Lv, Yuhao Shen, Xiao Sun, Xiaoyan Sun,
• Abstract summary: Speculative decoding (SD) has become a standard technique for accelerating LLM inference without sacrificing output quality.<n>We introduce TALON, a training-free, budget-driven adaptive tree expansion framework that can be plugged into existing tree-based methods.<n> TALON consistently outperforms state-of-the-art Eagle-3, achieving up to 5.16x end-to-end speedup over auto-regressive decoding.
• Score: 18.53532655905144
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Speculative decoding (SD) has become a standard technique for accelerating LLM inference without sacrificing output quality. Recent advances in speculative decoding have shifted from sequential chain-based drafting to tree-structured generation, where the draft model constructs a tree of candidate tokens to explore multiple possible drafts in parallel. However, existing tree-based SD methods typically build a fixed-width, fixed-depth draft tree, which fails to adapt to the varying difficulty of tokens and contexts. As a result, the draft model cannot dynamically adjust the tree structure to early stop on difficult tokens and extend generation for simple ones. To address these challenges, we introduce TALON, a training-free, budget-driven adaptive tree expansion framework that can be plugged into existing tree-based methods. Unlike static methods, TALON constructs the draft tree iteratively until a fixed token budget is met, using a hybrid expansion strategy that adaptively allocates the node budget to each layer of the draft tree. This framework naturally shapes the draft tree into a "deep-and-narrow" form for deterministic contexts and a "shallow-and-wide" form for uncertain branches, effectively optimizing the trade-off between exploration width and generation depth under a given budget. Extensive experiments across 5 models and 6 datasets demonstrate that TALON consistently outperforms state-of-the-art EAGLE-3, achieving up to 5.16x end-to-end speedup over auto-regressive decoding.

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