2025/11/11 by Ly Tran Ho Khanh, Khanh, Ly Tran Ho, Zhu, Dongxuan +4
Computer Science · Materials Science · #Advanced Graph Neural Networks #Artificial Intelligence (cs.AI) #Bottleneck #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Optimization problem #Point (geometry) #Process (computing) #Sampling (signal processing) #Stochastic Gradient Optimization Techniques #Synchronization (alternating current) #Variance (accounting)
paper · pdf · doi:10.48550/arxiv.2511.08305
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/11/11 · openalex created_date 2025/11/13 · openalex updated_date 2026/07/28
Best-of-N reasoning improves the accuracy of language models in solving complex tasks by sampling multiple candidate solutions and then selecting the best one based on some criteria. A critical bottleneck for this strategy is the output diversity limit, which occurs when the model generates similar outputs despite stochastic sampling, and hence recites the same error. To address this lack of variance in reasoning paths, we propose a novel unsupervised activation steering strategy that simultaneously optimizes the steering vectors for multiple reasoning trajectories at test time. At any synchronization anchor along the batch generation process, we find the steering vectors that maximize the total volume spanned by all possible intervened activation subsets. We demonstrate that these steering vectors can be determined by solving a Riemannian optimization problem over the product of spheres with a log-determinant objective function. We then use a Riemannian block-coordinate descent algorithm with a well-tuned learning rate to obtain a stationary point of the problem, and we apply these steering vectors until the generation process reaches the subsequent synchronization anchor. Empirical evaluations on popular mathematical benchmarks demonstrate that our test-time Riemannian activation steering strategy outperforms vanilla sampling techniques in terms of generative diversity and solution accuracy.