Want to dive deeper? This curriculum is covered in the following online courses: - XCS329 graduate course: https://online.stanford.edu/courses/cs329a-self-improving-ai-agents - Agentic AI professional education program: https://learn.stanford.edu/agentic-ai-2026.html Follow along with the course schedule and syllabus: https://cs329a.stanford.edu/ View the course playlist: https://www.youtube.com/playlist?list=PLangBM27OtEA Video summary: This lecture video from Stanford's CS329A, Self-Improving AI Agents, taught by Aakanksha Chowdhery on October 17, 2025, examines self-improvement through search. AlphaCode pretrains a masked language model on GitHub and CodeContests data and generates large numbers of samples before clustering and selecting a final answer, while AlphaCode2 fine-tunes Gemini Pro with a learned scoring model, reaching an 85th percentile ranking on competitive programming contests. The lecture explains how solve rate scales with sample budget and where selection and clustering become bottlenecks even after large-scale sample generation. It then introduces Search-O1, a method that triggers search queries when a reasoning model expresses uncertainty and reasons over retrieved documents, which outperforms standard and agentic retrieval-augmented generation on GPQA and multi-hop question-answering benchmarks including HotpotQA and Bamboogle. The session closes by comparing Search-O1's prompting-based approach to Search-R1's reinforcement-learning-based approach for teaching models when to search. Speaker Bio: Aakanksha Chowdhery Adjunct Professor of Computer Science, Stanford University Aakanksha is pushing the frontier of agentic LLMs by leveraging RL techniques to enable autonomous self-improving agents, especially in software engineering at the startup Reflection AI. At Stanford, she is co-teaching CS329A (Self-Improving AI agents) in Fall/Winter 2025 and is the Program Chair for MLSys 2026. Before this, she was the technical Lead of 540B PaLM model and lead researcher in Gemini at Google in pre-training, scaling, and finetuning of Large Language Models. She was also a core contributor in PaLM-E, MedPaLM, and Pathways project at Google. Prior to joining Google, She was technical lead for several interdisciplinary research initiatives at Microsoft Research and Princeton University across machine learning and distributed systems. She completed my PhD in Electrical Engineering from Stanford University and was awarded the Paul Baran Marconi Young Scholar Award for the outstanding scientific contributions of her dissertation in the field of communications and Internet.
Want to dive deeper? This curriculum is covered in the following online courses:
- XCS329 graduate course: https://online.stanford.edu/courses/cs329a-self-improving-ai-agents
- Agentic AI professional education program: https://learn.stanford.edu/agentic-ai-2026.html
Follow along with the course schedule and syllabus: https://cs329a.stanford.edu/
View the course playlist: https://www.youtube.com/playlist?list=PLangBM27OtEA
Video summary:
This lecture video from Stanford's CS329A, Self-Improving AI Agents, taught by Aakanksha Chowdhery on October 17, 2025, examines self-improvement through search. AlphaCode pretrains a masked language model on GitHub and CodeContests data and generates large numbers of samples before clustering and selecting a final answer, while AlphaCode2 fine-tunes Gemini Pro with a learned scoring model, reaching an 85th percentile ranking on competitive programming contests. The lecture explains how solve rate scales with sample budget and where selection and clustering become bottlenecks even after large-scale sample generation. It then introduces Search-O1, a method that triggers search queries when a reasoning model expresses uncertainty and reasons over retrieved documents, which outperforms standard and agentic retrieval-augmented generation on GPQA and multi-hop question-answering benchmarks including HotpotQA and Bamboogle. The session closes by comparing Search-O1's prompting-based approach to Search-R1's reinforcement-learning-based approach for teaching models when to search.
Speaker Bio:
Aakanksha Chowdhery
Adjunct Professor of Computer Science, Stanford University
Aakanksha is pushing the frontier of agentic LLMs by leveraging RL techniques to enable autonomous self-improving agents, especially in software engineering at the startup Reflection AI. At Stanford, she is co-teaching CS329A (Self-Improving AI agents) in Fall/Winter 2025 and is the Program Chair for MLSys 2026. Before this, she was the technical Lead of 540B PaLM model and lead researcher in Gemini at Google in pre-training, scaling, and finetuning of Large Language Models. She was also a core contributor in PaLM-E, MedPaLM, and Pathways project at Google. Prior to joining Google, She was technical lead for several interdisciplinary research initiatives at Microsoft Research and Princeton University across machine learning and distributed systems. She completed my PhD in Electrical Engineering from Stanford University and was awarded the Paul Baran Marconi Young Scholar Award for the outstanding scientific contributions of her dissertation in the field of communications and Internet.