arXiv:2510.26219v1 Announce Type: cross
Abstract: Test-time alignment of large language models (LLMs) attracts attention because fine-tuning LLMs requires high computational costs. In this paper, we propose a new test-time 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 demonstrate that the optimal mean is obtained by importance sampling with sampled rewards. AISP outperforms best-of-n sampling in terms of rewards over the number of used samples and achieves higher rewards than other reward-based test-time alignment methods.
Surrogate Neural Architecture Codesign Package (SNAC-Pack)
arXiv:2512.15998v1 Announce Type: cross Abstract: Neural Architecture Search is a powerful approach for automating model design, but existing methods struggle to accurately optimize for real