Stanford CME 295 Lecture 4 released: Teaching reinforcement learning with LLMs
The Stanford course CME 295 Transformers & Large Language Models has been updated to Lecture 4, Reinforcement learning with LLMs, and both the video and lecture notes are now public. This lecture goes from RL and Reward Design to PPO, GRPO, RLVR, and DAPO, explaining the notation in each one by one. The course is taught by Afshine and Shervine Amidi, with 9 lectures in total, covering Transformer architecture, attention variants, RoPE, LoRA fine-tuning, RLHF/GRPO training, Flash Attention inference optimization, MCP and AI Agents, and diffusion language models.
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Stanford CME 295 Lecture 4 explains the notation systems from RL to PPO, GRPO, RLVR, and DAPO in depth, making it suitable for systematically filling in the foundations of reinforcement learning.
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View original postStanford @stanfordnlp's new course CME 295, taught by the twin brothers @afshinea @shervinea, covers the entire LLM landscape in 9 lectures: from Transformer architecture, attention variants, and RoPE, to LoRA fine-tuning and RLHF/GRPO training, and then to Flash Attention inference optimization, MCP and AI Agents, and diffusion language models. Both the course and the well-known cheat sheets are fully public. https://t.co/FjHGTco5iS
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斯坦福 @stanfordnlp 新课 CME 295，双胞胎兄弟 @afshinea @shervinea 主讲，9 讲走完 LLM 全部版图：从 Transformer 架构、注意力变体、RoPE，到 LoRA 微调、RLHF/GRPO 训练，再到 Flash Attention 推理优化、MCP 与 AI Agent、扩散语言模型。 课程和知名速查表全部公开。 https://t.co/FjHGTco5iS
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Stanford's new course CME 295: Transformers & Large Language Models
@stanfordnlp The twin brothers (Afshine & Shervine Amidi @afshinea @shervinea) have reached Lecture 4 of their course: Reinforcement learning with LLMs
In this lecture, they go from RL and Reward Design all the way down to PPO, GRPO, RLVR, and DAPO, explaining every symbol clearly—perfect for systematically filling in your RL knowledge!