Building a Reasoning Machine: An Open-Source Journey 🧠

Have you ever wondered how AI learns to “think”? It’s not magic, it’s data! Join us as we attempt to recreate the magic of reasoning AI like ChatGPT, in a more transparent and accessible way.

🧩 The Puzzle of Reasoning AI

Current leading AI models like ChatGPT seem to reason incredibly well, but their inner workings are a black box. We aim to demystify this process and empower the open-source community to contribute.

🌱 From Autocomplete to Reasoning: The Data Evolution

Think of early AI models like GPT2 as glorified autocomplete engines, recognizing and replicating patterns in text. Over time, through clever data engineering and training techniques, they’ve evolved:

Vanilla Models: Basic pattern recognition (GPT2, GPT3).
Instruct Models: Follow single instructions (InstructGPT).
Chatbots: Engage in iterative conversations (ChatGPT).
Reasoning AI: Solve complex problems through logical steps.

Each generation builds upon the last, leveraging data from the previous stage to unlock new capabilities.

🤖 Claude: Our Reasoning Ally

The AI model Claude, developed by Anthropic, already exhibits promising reasoning abilities. By carefully prompting Claude with specific problems and instructions, we can observe its thought process and guide it towards more accurate solutions.

Example: We challenged Claude to create a 10-word sentence where each word has one letter more than the previous one. Through trial and error, and with minimal guidance, Claude successfully solved the problem!

This demonstrates that existing AI models, with the right prompting, can be used to generate high-quality training data for specialized reasoning tasks.

🏗️ Building the Reasoning Dataset: A 4-Step Process

Our approach to building a reasoning AI involves a four-step process:

Synthesize Questions: Generate a large volume of diverse, challenging questions that require reasoning.
Synthesize Conversations: Use Claude to solve these questions, capturing its step-by-step reasoning process as conversational data.
Verify Solutions: Employ Claude’s code and math capabilities to automatically check the accuracy of its own solutions, ensuring data quality.
Clean & Format Data: Transform the raw conversation data into a structured format suitable for fine-tuning a dedicated reasoning AI model.

By automating this process, we can create a massive dataset of reasoning examples, potentially surpassing the capabilities of existing models.

🚀 Join the Open-Source Revolution!

This is just the beginning of our journey. We invite you to follow our progress, participate in discussions, and contribute to this exciting open-source initiative!