arxiv preprint – Iterative Reasoning Preference Optimization

In this episode, we discuss Iterative Reasoning Preference Optimization by Richard Yuanzhe Pang, Weizhe Yuan, Kyunghyun Cho, He He, Sainbayar Sukhbaatar, Jason Weston. 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. The technique uses a specialized loss function, incorporating negative log-likelihood, to systematically refine the reasoning accuracy of AI responses. It has been tested on a Llama-2-70B-Chat model and demonstrated significant performance improvements across different reasoning benchmarks without the need for additional external data.