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Having worked on Reinforcement Learning, itās always fascinating to see how itās being applied in the world of LLMs. If youāre curious about how RL powers modern LLM agents, especially in areas like reward modeling, and policy gradients here are a few great resources Iād highly recommend š š Foundational Resource 1. Sutton & Barto ā Reinforcement Learning: An Introduction This is the RL bible. The OG textbook. If youāre 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, Iāve found reward modeling and policy gradient optimization to be the trickiest parts in RL. Have you explored RL before?
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Give me 2 minutes, I will tell you How to Learn Reinforcement Learning for LLMs A humorous analogy for reinforcement learning uses cake as an example.Ā Reinforcement learning, much like baking a cake, involves trial and error to achieve a desired ou
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A (Long) Peek into Reinforcement Learning How do AI agents master games like Go, control robots, or optimize trading strategies? The answer lies in Reinforcement Learning (RL)āwhere agents learn by interacting with environments to maximize rewards.
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LLM Post-Training: A Deep Dive into Reasoning LLMs This survey paper provides an in-depth examination of post-training methodologies in Large Language Models (LLMs) focusing on improving reasoning capabilities. While LLMs achieve strong performance
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The ultimate AI/ML roadmap for beginners š š š®ššµš What to learn: ⢠Linear Algebra ⢠Calculus ⢠Statistics Resources: ⢠Practical Statistics for Data Science( https://amzn.to/446czl5 ) ⢠Mathematics for Machine Learning( https://amzn.to/441s
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AI Resources for Beginners Books: 1. Deep Learning. Illustrated Edition. Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2. Mathematics for Machine Learning. Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. 3. Reinforcement learning, An Introd
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Top 10 AI Research Papers Since 2015 š§ 1. Attention Is All You Need (Vaswani et al., 2017) Impact: Introduced the Transformer architecture, revolutionizing natural language processing (NLP). Key contribution: Attention mechanism, enabling models
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