[2404. 19733] Iterative Reasoning Preference Optimization In this work we develop an iterative approach that optimizes the preference between competing generated Chain-of-Thought (CoT) candidates by optimizing for winning vs losing reasoning steps that lead to the correct answer
Iterative Reasoning Preference Optimization - OpenReview In this work, we develop an approach to apply iterative preference optimization to reasoning tasks, with a particular focus on Chain-of-Thought (CoT) reasoning [Wu et al , 2023]
Paper page - Iterative Reasoning Preference Optimization In this work we develop an iterative approach that optimizes the preference between competing generated Chain-of-Thought (CoT) candidates by optimizing for winning vs losing reasoning steps that lead to the correct answer
arxiv preprint – Iterative Reasoning Preference Optimization This study explores a new iterative method aimed at improving how AI models generate step-by-step logical reasoning, or Chain-of-Thought (CoT), to reach correct answers by optimizing between competing reasoning steps
Abstract arXiv:2404. 19733v3 [cs. CL] 26 Jun 2024 1: Iterative Reasoning Preference Optimization Our iterative preference optimization method consists of two steps: (i) Chain-of-Thought Answer Generation: training prompts are used to generate candidate reasoning steps and answers from model Mt, and then the answers are ev