@Xudong07452910: Recently discovered that Stanford Online posted the full lecture recordings of the graduate course CS329A: Self-Improving AI Agents to YouTube, 9 lectures in total. The course basically revolves around one question: how AI agents can continuously improve through interaction with their environment...
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Stanford University has released the full recordings of the graduate course CS329A: Self-Improving AI Agents on YouTube, with 9 lectures covering topics such as test-time compute, verifiers, reinforcement learning, tool feedback, and multi-step reasoning, systematically organizing the direction of agent self-improvement.
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Cached at: 08/11/26, 03:39 AM
Recently I discovered that Stanford Online has posted the full lecture recordings for the graduate course CS329A: Self-Improving AI Agents on YouTube — 9 lectures in total.
The course revolves around one core question: how AI agents can continuously improve their capabilities through ongoing interaction with the environment.
The content starts with test-time compute, verifiers, and RL, then moves on to tool/code feedback, multi-step reasoning and planning, deep research, self-improvement, and evaluating agents on long-horizon tasks.
What I really like about it is that it ties together many directions currently scattered across papers — from “how to make agents think more” all the way to “how to verify results, learn from feedback, and then put these capabilities into longer tasks.”
If you’ve been keeping an eye on agent self-evolution lately, this course is well worth a systematic review.
YouTube:
@Xudong07452910: Recently I discovered that Stanford Online has posted the full lecture recordings for the graduate course CS329A: Self-Improving AI Agents on YouTube — 9 lectures in total. The course revolves around one core question: how AI agents can continuously improve their capabilities through ongoing interaction with the environment…
Channel: @Xudong07452910 Source: https://www.youtube.com/playlist?list=PLangBM27OtEA
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