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# Part 3: The Forge-Monarch Connection
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This is part 3 of our series, in the previous sections: we learned Part 1: [RL Concepts and how they map to Forge](./1_RL_and_Forge_Fundamentals.MD), Part 2: [Forge Internals](./2_Forge_Internals.MD).
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This is part 3 of our series, in the previous sections: we learned Part 1: [RL Concepts and how they map to Forge](./1_RL_and_Forge_Fundamentals), Part 2: [Forge Internals](./2_Forge_Internals).
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Now let's peel back the layers. Forge services are built on top of **Monarch**, PyTorch's distributed actor framework. Understanding this connection is crucial for optimization and debugging.
# Zero to Forge: From RL Theory to Production-Scale Implementation
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A comprehensive guide for ML Engineers building distributed RL systems for language models.
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Some of the examples mentioned below will be conceptual in nature for understanding. Please refer to API Docs (Coming Soon!) for more details
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Welcome to the Tutorials section! This section is inspired by the A-Z PyTorch tutorial, shoutout to our PyTorch friends that remember!
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## Tutorial Structure
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This section currently is structured in 3 detailed parts:
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1.[RL Fundamentals and Understanding Forge Terminology](tutorials/zero-to-forge/1_RL_and_Forge_Fundamentals): This gives a quick refresher of Reinforcement Learning and teaches you Forge Fundamentals
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2.[Forge Internals](tutorials/zero-to-forge/2_Forge_Internals): Goes a layer deeper and explains the internals of Forge
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3.[Monarch 101](tutorials/zero-to-forge/3_Monarch_101): It's a 101 to Monarch and how Forge Talks to Monarch
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Each part builds upon the next and the entire section can be consumed in roughly an hour - Grab a Chai and Enjoy!
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If you're eager, please checkout our SFT Tutorial too (Coming soon!)!
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