Autopsv: Automated process-supervised verifier

J Lu, Z Dou, H Wang, Z Cao, J Dai… - Advances in Neural …, 2025‏ - proceedings.neurips.cc
In this work, we propose a novel method named\textbf {Auto} mated\textbf {P} rocess-\textbf
{S} upervised\textbf {V} erifier (\textbf {\textsc {AutoPSV}}) to enhance the reasoning …

Learning from correctness without prompting makes LLM efficient reasoner

Y Yao, H Wu, Z Guo, B Zhou, J Gao, S Luo… - arxiv preprint arxiv …, 2024‏ - arxiv.org
Large language models (LLMs) have demonstrated outstanding performance across various
tasks, yet they still exhibit limitations such as hallucination, unfaithful reasoning, and toxic …

Formal mathematical reasoning: A new frontier in ai

K Yang, G Poesia, J He, W Li, K Lauter… - arxiv preprint arxiv …, 2024‏ - arxiv.org
AI for Mathematics (AI4Math) is not only intriguing intellectually but also crucial for AI-driven
discovery in science, engineering, and beyond. Extensive efforts on AI4Math have mirrored …

Beyond Limited Data: Self-play LLM Theorem Provers with Iterative Conjecturing and Proving

K Dong, T Ma - arxiv preprint arxiv:2502.00212, 2025‏ - arxiv.org
A fundamental challenge in formal theorem proving by LLMs is the lack of high-quality
training data. Although reinforcement learning or expert iteration partially mitigates this issue …

Diverse Inference and Verification for Advanced Reasoning

I Drori, G Longhitano, M Mao, S Hyun, Y Zhang… - arxiv preprint arxiv …, 2025‏ - arxiv.org
Reasoning LLMs such as OpenAI o1, o3 and DeepSeek R1 have made significant progress
in mathematics and coding, yet find challenging advanced tasks such as International …