Practical AI Solutions for Improving Reasoning Tasks in Language Models
Iterative Preference Optimization
Harness the power of Iterative Preference Optimization to enhance reasoning tasks in Language Models. Our approach delivers substantial enhancements in reasoning capabilities without the need for human-in-the-loop or extra training data, ensuring simplicity and efficiency.
With our method, each iteration generates multiple responses and constructs preference pairs based on the correctness of the final answer. We utilize a modified DPO loss with an additional NLL term for training, leading to escalating accuracy and improved reasoning prowess over successive iterations.
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