Stanford CS329A: Self-Improving AI Agents Course Overview Notes
The takeaway is that small models can beat larger ones when given enough parallel samples and a verifier, which reframes inference-time compute as the practical lever for builders without trillion-parameter budgets. Engineers designing agent pipelines should note the coverage-versus-pass@k distinction when choosing between repeated sampling and reasoning models like o1. Stanford publishing the full lecture makes a frontier-lab curriculum freely accessible to self-taught practitioners.
