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@yoavg
yoavg / llm-materials-2025.md
Last active July 16, 2025 03:43

Learning LLMs in 2025

So you know how the transformer works, and you know basic ML/DL, and you want to learn more about LLMs. One way to go is looking into the various "algorithmic" stuff (optimization algorithms, RL, DPO, etc). Lot's of materials on that. But the interesting stuff is (in my opinion at least) not there.

This is an attempt to collect a list of academic (or academic-like) materials that explore LLMs from other directions, and focus on the non-ML-algorithmic aspects.

Courses

  • David Chiang's Theory of Neural Networks course.
  • This is not primarily LLMs, but does have substantial section on Transformers. Formal/Theory. More of a book than a course.
@ekzhang
ekzhang / Buildcarte.ipynb
Last active April 7, 2025 00:59
Build Systems à la Carte — Python edition
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@yoavg
yoavg / rl-for-llms.md
Last active July 17, 2025 14:51

Reinforcement Learning for Language Models

Yoav Goldberg, April 2023.

Why RL?

With the release of the ChatGPT model and followup large language models (LLMs), there was a lot of discussion of the importance of "RLHF training", that is, "reinforcement learning from human feedback". I was puzzled for a while as to why RL (Reinforcement Learning) is better than learning from demonstrations (a.k.a supervised learning) for training language models. Shouldn't learning from demonstrations (or, in language model terminology "instruction fine tuning", learning to immitate human written answers) be sufficient? I came up with a theoretical argument that was somewhat convincing. But I came to realize there is an additional argumment which not only supports the case of RL training, but also requires it, in particular for models like ChatGPT. This additional argument is spelled out in (the first half of) a talk by John Schulman from OpenAI. This post pretty much

@dangpzanco
dangpzanco / climate_change_ai.md
Last active June 13, 2020 12:23
"Climate Change: How Can AI Help?" ICML2019 Workshop Highlights