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arXiv · 2506.04118

Guided Speculative Inference for Efficient Test-Time Alignment of LLMs

Jonathan Geuter·Youssef Mroueh·David Alvarez-Melis
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Abstract

We propose Guided Speculative Inference (GSI), a novel algorithm for efficient reward-guided decoding in large language models. GSI combines soft best-of-$n$ test-time scaling with a reward model $r(x,y)$ and speculative samples from a small auxiliary model $π_S(y\mid x)$. We provably approximate both the optimal tilted policy $π_{β,B}(y\mid x) \propto π_B(y\mid x)\exp(β\,r(x,y))$ of soft best-of-$n$ under the base model $π_B$, as well as the expected reward under the optimal policy. In experiments on reasoning benchmarks (MATH500, OlympiadBench, Minerva Math, MMLU-STEM, GSM8K) and across different model families, our method achieves higher accuracy than standard soft best-of-$n$ with $π_S$ and reward-guided speculative decoding (Liao et al., 2025), and in certain settings even outperforms soft best-of-$n$ with $π_B$, while reducing end-to-end latency by up to $28\%$. The code is available at https://github.com/j-geuter/GSI .

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BibTeXRIS

Jonathan Geuter, Youssef Mroueh, David Alvarez-Melis. 2026-04-27. Guided Speculative Inference for Efficient Test-Time Alignment of LLMs. https://arxiv.org/abs/2506.04118

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