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Bhoop singh Gurjar

AI Deep Explorer | f...ย โ€ขย 20d

Having worked on Reinforcement Learning, itโ€™s always fascinating to see how itโ€™s being applied in the world of LLMs. If youโ€™re curious about how RL powers modern LLM agents, especially in areas like reward modeling, and policy gradients here are a few great resources Iโ€™d highly recommend ๐Ÿ‘‡ ๐ŸŽ“ Foundational Resource 1. Sutton & Barto โ€“ Reinforcement Learning: An Introduction This is the RL bible. The OG textbook. If youโ€™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, Iโ€™ve found reward modeling and policy gradient optimization to be the trickiest parts in RL. Have you explored RL before?

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Bhoop singh Gurjar

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My Favorite AI & ML Books That Shaped My Learning Over the years, Iโ€™ve read tons of books in AI, ML, and LLMs โ€” but these are the ones that stuck with me the most. Each book on this list taught me something new about building, scaling, and underst

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Bhoop singh Gurjar

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