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/ Aakanksha Chowdhery Adjunct Professor of Computer Science, Stanford University View the course playlist: https://www.youtube.com/playlist?list=PLangBM27OtEA Video Summary: This lecture recording from Stanford's CS329A, Self-Improving AI Agents, taught by Aakanksha Chowdhery on October 3, 2025, covers three approaches to improving language models through feedback. ReAct interleaves chain-of-thought reasoning with tool-calling actions and is evaluated on HotpotQA, FEVER, and WebShop. RLEF, or Reinforcement Learning from Execution Feedback, trains coding agents using public and private unit test results within a PPO training loop, evaluated on CodeContests. Constitutional AI, developed by Anthropic, uses a written set of principles along with model self-critique and revision to train a preference model through reinforcement learning from AI feedback rather than human feedback. The lecture compares how each method sources its feedback signal, from environment interaction to execution results to AI-generated critique, and reviews related work including WebGPT, Code Monkeys, and SWE-bench. Speaker Bio: 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/
Aakanksha Chowdhery
Adjunct Professor of Computer Science, Stanford University
View the course playlist: https://www.youtube.com/playlist?list=PLangBM27OtEA
Video Summary:
This lecture recording from Stanford's CS329A, Self-Improving AI Agents, taught by Aakanksha Chowdhery on October 3, 2025, covers three approaches to improving language models through feedback. ReAct interleaves chain-of-thought reasoning with tool-calling actions and is evaluated on HotpotQA, FEVER, and WebShop. RLEF, or Reinforcement Learning from Execution Feedback, trains coding agents using public and private unit test results within a PPO training loop, evaluated on CodeContests. Constitutional AI, developed by Anthropic, uses a written set of principles along with model self-critique and revision to train a preference model through reinforcement learning from AI feedback rather than human feedback. The lecture compares how each method sources its feedback signal, from environment interaction to execution results to AI-generated critique, and reviews related work including WebGPT, Code Monkeys, and SWE-bench.
Speaker Bio:
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.