rkinas/reasoning_models_how_to
This repository serves as a collection of research notes and resources on training large language models (LLMs) and Reinforcement Learning from Human Feedback (RLHF). It focuses on the latest research, methodologies, and techniques for fine-tuning language models.
This collection of research notes and resources helps AI researchers and engineers understand and implement the latest techniques for training large language models (LLMs) and applying Reinforcement Learning from Human Feedback (RLHF). It compiles academic papers, video lectures, and practical implementations, allowing users to efficiently learn and apply methods like PPO, DPO, and KTO to refine their language models. The primary users are professionals working on model alignment and LLM fine-tuning.
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Use this if you are an AI researcher or engineer focused on improving large language model performance through advanced training methods, especially those involving human feedback.
Not ideal if you are looking for a plug-and-play solution to use an existing LLM without delving into its core training and alignment methodologies.
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Jul 28, 2025
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