Papers › Guided Speculative Inference for Efficient Test-Time Alignment of LLMs

Guided Speculative Inference for Efficient Test-Time Alignment of LLMs

4 Jun 2025arXiv:2506.04118archive 2025-07-28

Jonathan Geuter, Youssef Mroueh, David Alvarez-Melis

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|x). We provably approximate the optimal tilted policy π_(β,B)(y|x) ∝π_B(y|x)exp(β r(x,y)) of soft best-of-n under the primary model π_B. We derive a theoretical bound on the KL divergence between our induced distribution and the optimal policy. In experiments on reasoning benchmarks (MATH500, OlympiadBench, Minerva Math), 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. The code is available at https://github.com/j-geuter/GSI .

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