Stanford OnlineStanford CME295 Transformers & LLMs | Autumn 2025 | Lecture 9 - Recap & Current Trends
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At a glance
WHAT IT’S REALLY ABOUT
Stanford CME295 Final Lecture: Course Recap, Vision Transformers, Diffusion LLMs, Future Trends
- The first part recaps the course from tokenization, embeddings and self-attention through transformer variants, LLM training, preference tuning, reasoning, RAG, tool calling and evaluation.
- The second part shows transformers extending beyond text through Vision Transformers and vision-language models, which feed image tokens alongside text tokens into an LLM.
- Diffusion-based LLMs replace noise with mask tokens and generate text by iterative unmasking, which can need far fewer forward passes than autoregressive decoding.
- The closing section argues that design choices like optimizers, normalization, activations and data curation are still evolving, and that cost-efficiency, hardware and model collapse are active concerns.
- The lecturers look ahead to democratized agentic tools, AI-assisted browsing and unsolved challenges such as continual learning and hallucination, and list resources for staying current.
IDEAS WORTH REMEMBERING
5 ideasSelf-attention is the central idea of the course
It lets tokens attend to one another regardless of position, overcoming the long-range dependency limits of RNNs, and it maps efficiently onto modern hardware.
GRPO simplifies RL training for reasoning models
It drops the value model by comparing several completions against each other, and with verifiable rewards like math answers it needs no reward model either, leaving only the policy and reference models.
Transformers work on images when given enough data
ViT splits an image into patches, encodes them like BERT with a CLS token, and outperforms convolutional networks despite having much lower inductive bias.
Diffusion LLMs use masking in place of noise
Because text tokens are discrete, noise is replaced by mask tokens. The model starts from a fully masked sequence and refines it in a fixed number of steps, which can be much faster, with some benchmarks claiming around 10X.
Autoregressive generation is sequential only at inference
Training can be parallelized thanks to the causal mask, but inference must produce each token after the previous one, which motivates alternatives like diffusion.
WORDS WORTH SAVING
5 quotesnoise is to images what the mask token is to text.
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You type anything you want in your favorite search browser, chances are the first results are eighty percent LLM-generated.
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So I would just encourage you, after this class, to also keep an open mind for non-text related transformer applications.
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brainstorming the concepts that you learn is great for actually grasping the concepts of interest, and getting that early feedback loop is, uh, very useful for learning.
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So it tries to have a coarse-to-fine refinement of the output.
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