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Explore Stanford CME295 Transformers & LLMs | Autumn 2026 | Lecture 3 - LLM training with 61,205 views, 566 likes, and 36 comments. Experience the impact of this video content that has captured audience attention.

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education To follow along with the course schedule and syllabus, visit: https://cme295.stanford.edu/syllabus/ Chapters: 00:00:00 Introduction 00:03:36 Pretraining 00:10:33 Cross-entropy loss 00:17:15 Data mixtures 00:22:18 Midtraining 00:23:41 Long context training 00:31:47 Supervised finetuning (SFT) 00:40:15 Low-Rank Adaptation (LoRA) 00:46:37 Preference tuning 00:54:44 Bradley-Terry formulation 01:01:49 Reinforcement Learning from Human Feedback (RLHF) 01:05:38 Direct Preference Preference Optimization (DPO) 01:12:08 Reasoning 01:20:56 Group Relative Policy Optimization (GRPO) 01:23:31 On-Policy Distillation (OPD) 01:27:47 End-to-end example Afshine Amidi is an Adjunct Professor at Stanford University. Shervine Amidi is an Adjunct Professor at Stanford University. Follow the playlist: https://youtube.com/playlist?list=PLakGfbKCei80&si=HF2tNywj7nXIQq8X

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