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Applications

End-to-end guides for formulating real-world problems as reinforcement learning. State, action, reward design and deployment considerations.

Language & Agents

01

RLHF for Language Models

How to formulate language model alignment as an RL problem. State, action, reward design for training helpful and harmless AI assistants.

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02

Fine-Tuning Your First LLM with RL

A hands-on guide to fine-tuning a small language model using reinforcement learning. Build intuition by training a real model end-to-end.

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Operations & Systems

01

Elevator Dispatch with Multi-Agent RL

How to formulate elevator dispatch as a multi-agent RL problem. Minimizing wait times through coordination without communication.

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Content Status

📝 AI Generated — Pending review
✅ Editor Reviewed — Approved by editor
👥 Community Reviewed — Incorporates feedback
🔒 Verified — Code tested, demos working