斯坦福 CS329A Self-Improving AI Agents 全部 9 讲视频上线 YouTube

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.

shao__meng@shao__mengSource

Most viewers quit after the first lecture, so finishing all 9 gives systematic grounding that ad-hoc prompting cannot replace. Builders designing agent training loops or evaluating self-improvement workflows will get the most value. Since the lectures are free on YouTube, anyone in Singapore or the wider region can follow along without access barriers.

shao__meng@shao__meng11 sourcesSource

Explains why coding agents advance faster than writing assistants through a complete self-improvement cycle.

shao__meng@shao__mengSource