Course Notes | Columbia Quant LLM Reading Group

Public Course Pages

Language modeling from scratch and modern LLM systems.

Core machine learning background.

Machine learning theory reference and course-page style.

Introductory theory materials.

Major reading object for the group; Citadel Paper Award work.

Reference standard for research taste, clarity, and technical ambition.

Reasoning, coding, agentic intelligence, and model-system reports.

Foundation model, multimodal, and large-scale systems research.

Open model reports, post-training, safety, and agentic systems.

Shared notes to be added on evaluation, reward models, verifiers, RL, and deployment feedback loops.

Community discussion that motivated the launch of the reading group.

Background reading for students entering the theory side of LLM research.

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