Having worked on Reinforcement Learning, it’s always fascinating to see how it’s being applied in the world of LLMs. If you’re curious about how RL powers modern LLM agents, especially in areas like reward modeling, and policy gradients here are a few great resources I’d highly recommend 👇 🎓 Foundational Resource 1. Sutton & Barto – Reinforcement Learning: An Introduction This is the RL bible. The OG textbook. If you’re serious about RL, this is the place to start. Link - https://amzn.to/42XqCs5 2. Maxim Lapan - Deep Reinforcement Learning Hands-On (3rd Edition) A hands-on book that makes it easier to move from concepts to implementation. Link - https://amzn.to/44D12tG 3. Nathan Lambert - Reinforcement Learning from Human Feedback Perfect for understanding how RL is applied to align large language models. Link - https://rlhfbook.com/ Personally, I’ve found reward modeling and policy gradient optimization to be the trickiest parts in RL. Have you explored RL before?
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