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
The increasing computational cost of fine-tuning large language models drives research into more efficient test-time alignment methods, making this technical advancement timely.
This development allows for more adaptive and cost-effective deployment of advanced AI models, reducing the economic and computational barriers to their widespread use and customization.
The ability to align LLMs at test-time without extensive re-fine-tuning enables LLMs to adapt more flexibly and affordably to dynamic user preferences or specific task requirements.
- · AI developers
- · Cloud computing providers
- · Enterprises adopting AI
- · Researchers in machine learning
- · Companies reliant on outdated fine-tuning methods
- · High-latency AI applications
Reduced operational costs and increased adaptability for AI systems.
Faster iteration cycles for AI product development and deployment across various industries.
Enhanced competition in applied AI, leading to a broader array of customized and efficient AI solutions.
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