Test-time reward-guided alignment of language models by importance sampling on pre-logit space

arXiv:2510.26219v3 Announce Type: replace Abstract: Test-time alignment of large language models (LLMs) attracts attention because fine-tuning of LLMs requires high computational costs. In this paper, we propose a new test-time reward-guided alignment method called adaptive importance sampling on pre-logits (AISP) on the basis of the sampling-based model predictive control with the stochastic control input. AISP applies the Gaussian perturbation into pre-logits, which are outputs of the penultimate layer, so as to maximize expected rewards with respect to the mean of the perturbation. We demon

Source: arXiv cs.LG — read the full report at the original publisher.

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