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Introduction In the rapidly evolving world of artificial intelligence, reinforcement learning (RL) stands out as a powerful framework for training AI agents to make decisions in complex and dynamic environments. However, traditional RL algorithms often come with a significant drawback: they are highly specialized and require meticulous tuning for each specific task, making them less adaptable and more resource-intensive. Enter MR.Q (Model-based Representations for Q-learning)—a groundbreaking advancement in the field of reinforcement learning.

Summary Forecasting future events is a critical task in fields like finance, politics, and technology. However, improving the forecasting abilities of large language models (LLMs) often requires extensive human supervision. In this post, we explore a novel approach from the paper LLMs Can Teach Themselves to Better Predict the Future that enables LLMs to teach themselves better forecasting skills using self-play and Direct Preference Optimization (DPO). We’ll walk through a Python implementation of this method, step by step.