arXiv:2608.30372v1 Announce Type: new
Abstract: As Large Language Models rapidly advance, performance on standard multiple-choice question answering (MCQA) benchmarks is reaching saturation. While th...
By Minsoo Song, Chanjun Park
Selecting the best response from multiple small-model samples using a stronger scorer is a simple inference-time strategy, but fails when the small model has already committed to incorrect reasoning paths. PRM guided search avoids this by scoring candidate continuations during generation, but requires a reward model trained with step-level labels.
arXiv:2610.25768v2 Announce Type: replace
Abstract: Large language models (LLMs) often generate more reasoning tokens than needed. Limiting the reasoning budget reduces cost, but forcing an answer fr...
By Zhongyin Wang, Zhaoqian Xue, Junting Mu, Haozheng Liu, Ye Yuan, Haolun Wu, Lingyao Li
The paper introduces Length‑Conditioned Majority Voting (LCMV), a test‑time training method that enhances the standard Majority Voting (MV) technique for Large Language Models (LLMs). LCMV incorporates an empirical bias that longer Chain‑of‑Thoughts (CoTs) are less likely to be correct by modeling a length‑conditioned prior with a logistic function. This prior is adapted on unlabeled test data without fine‑tuning the LLM, and the resulting weighted votes consistently outperform standard MV and other length‑ or confidence‑based aggregation strategies across multiple reasoning benchmarks.
By Fiorenzo Parascandolo, Enver Sangineto, Xiaoyi Yu, Wenhui Tan, Jipei He, Ruihua Song, Rita Cucchiara
The paper introduces a Bayesian Bradley‑Terry model (BBTM) that employs Polya‑Gamma data augmentation for efficient Gibbs sampling and quantifies uncertainty in latent abilities and ranks. Three active‑learning strategies based on the knowledge‑gradient principle are proposed, using Kendall’s tau distance, posterior rank entropy, and posterior separation as acquisition functions, with approximate one‑step updates to reduce computational cost. The authors also present Posterior Separation Reliability (PSR) as an internal indicator of ranking reliability, and demonstrate through synthetic experiments that BBTM outperforms preference‑based Bayesian comparative judgement, with the entropy‑based strategy excelling at low‑to‑moderate noise and the posterior‑separation strategy at high noise levels.
By Saman Jayasinghe, Sean Walton, Alma Rahat, Andy Gray, Chris Wheadon
Majority Voting (MV) is a simple and widely used strategy which improves the reasoning accuracy of Large Language Models (LLMs) by allocating additional inference-time computation. Specifically, in MV...