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MR.Q: Model-Based Representations for Model-Free Trading

Tue, 18 Mar 2025 13:05:34 +0000

Introduction

Model-free reinforcement learning learns a policy directly from experience, but it can struggle to discover useful representations from sparse or noisy rewards. Model-based reinforcement learning receives a denser training signal by learning how states, actions, rewards, and termination relate to one another, but it often pays for that knowledge through planning complexity and model error.

MR.Q: MR.Q

Can a model-free agent keep the representation-learning benefits of a learned model without using that model to plan?

Self-Learning LLMs for Stock Forecasting: A Python Implementation with Direct Preference Optimization

Thu, 13 Feb 2025 14:22:06 +0000

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.