ucla rl-llm

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1. 1▶[UCLA RL-LLM] Chapter 2.4: In-context learning and instruction fine-tuningReinforcement Learning of Large Language ModelsNotes
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2. 2▶[UCLA RL-LLM] Chapter 2.3: Transformers II (modern transformers updates and sampling methods)Reinforcement Learning of Large Language ModelsNotes
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3. 3▶[UCLA RL-LLM] Chapter 2.2: Transformers I (BERT, GPT-1)Reinforcement Learning of Large Language ModelsNotes
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4. 4▶[UCLA RL-LLM] Chapter 2.1: NLP foundations, language modeling, RNNsReinforcement Learning of Large Language ModelsNotes
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5. 5▶[UCLA RL-LLM] Chapter 3.2: Reinforcement learning with verifiable rewards (RLVR)Reinforcement Learning of Large Language ModelsNotes
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6. 6▶[UCLA RL-LLM] Chapter 3.1: Reinforcement learning from human feedback (PPO, DPO)Reinforcement Learning of Large Language ModelsNotes
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7. 7▶[UCLA RL-LLM] Chapter 1.5: AlphaGo, test-time compute, and expert iterationReinforcement Learning of Large Language ModelsNotes
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8. 8▶[UCLA RL-LLM] Chapter 1.4: Deep policy gradient methods (PPO, GRPO)Reinforcement Learning of Large Language ModelsNotes
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9. 9▶[UCLA RL-LLM] Chapter 1.3: Deep policy gradient methods (A3C)Reinforcement Learning of Large Language ModelsNotes
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10. 10▶[UCLA RL-LLM] Chapter 1.2: Deep policy evaluationReinforcement Learning of Large Language ModelsNotes
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11. 11▶[UCLA RL-LLM] Chapter 1.1: MDP foundations, imitation learning, and value iterationReinforcement Learning of Large Language ModelsNotes
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12. 12▶[UCLA RL-LLM] Chapter 0: Course outline and prologueReinforcement Learning of Large Language ModelsNotes
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