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✨ Introduction: Real-Time Self-Tuning with MR.Q

Most machine learning models need hundreds of examples, large GPUs, and hours of training to learn anything useful. But what if you could build a system that gets smarter with just a handful of preference examples, runs entirely on your CPU, and improves while you work?

That’s exactly what MR.Q offers.

🔍 It doesn’t require full retraining.
⚙️ It doesn’t modify the base model.
🧠 It simply learns how to judge quality — and does it fast.

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.