MRQ on AIBussin — AI applications, systems and books

Building a Self-Improving Chain-of-Thought Agent: Local LLMs Meet the CoT Encyclopedia

Wed, 21 May 2025 18:36:51 +0100

Most AI systems generate answers. Ours examines how they think. This isn’t just prompt engineering this is structured reasoning at scale.

🔧 Summary

Large Language Models are transforming every field, yet their internal reasoning remains a formidable black box. We can get brilliant outputs, but without understanding how those conclusions were reached, we’re left guessing how to improve, debug, or even trust them. This opacity limits our ability to build truly reliable and self-improving AI systems.

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?