Stanford CS329A Self-Improving AI Agents | Part 4 | Learning from Feedback with Tools/Code

Stanford CS329A Self-Improving AI Agents | Part 4 | Learning from Feedback with Tools/Code

by Aakanksha Chowdhery

Formal & Physical Sciences AI — Models & Research UYQArtificial intelligence (AI)UYQMMachine learning
🎙 Aakanksha Chowdhery 👥 1.2M 📅 August 3, 2026 ⏱ 71 min 👁 2K 📄 lecture 🧭 2026-08-04
Available in: English (current) Français

Keywords

ReActRLEFConstitutional AItool callingexecution feedback

Summary

This lecture from Stanford’s CS329A course, taught by Aakanksha Chowdhery, covers three approaches for improving language models through feedback. The first is ReAct, which interleaves reasoning and acting by prompting the model to generate thoughts and then take actions, grounding it in real-world environments. It is evaluated on HotpotQA, FEVER, and WebShop. The second is RLEF (Reinforcement Learning from Execution Feedback), which trains coding agents using unit test results within a PPO loop, evaluated on CodeContests. The third is Constitutional AI, developed by Anthropic, which uses a set of principles and model self-critique to train a preference model via reinforcement learning from AI feedback. The lecture compares how each method sources its feedback signal: environment interaction, execution results, and AI-generated critique. It also reviews related work including WebGPT, Code Monkeys, and SWE-bench. The speaker emphasizes the importance of grounding and interpretability in AI agents.

144 words

Critical Evaluation

The lecture provides a solid overview of three influential methods for self-improving AI agents, each representing a different paradigm for learning from feedback. The explanations are clear and well-structured, with concrete examples (e.g., the HotpotQA question about Apple remote) that illustrate the concepts effectively. The speaker’s expertise is evident, and the content is grounded in published research, lending credibility to the presentation. However, the lecture is primarily a survey rather than a deep dive into any single method; it does not provide detailed mathematical formulations or implementation specifics, which may limit its usefulness for practitioners seeking to implement these techniques. The discussion of related work is brief, and the lecture does not critically compare the methods’ limitations or trade-offs in depth. The adéquation between title and content is strong, as the lecture indeed focuses on learning from feedback with tools and code. Overall, the lecture is informative and well-delivered, but it could benefit from more critical analysis and practical guidance.

160 words

Title / Content Match

The title accurately reflects the content: a lecture on self-improving AI agents, specifically focusing on learning from feedback via tools and code.

Quality & Reliability

8/10

Lecture from Stanford University by an expert in the field, covering well-established research papers (ReAct, RLEF, Constitutional AI) with clear explanations and examples. The content is based on published research and the speaker's expertise, but lacks direct citations to primary sources within the video itself.

Key Moments

Markers derived by PSI from the transcript: the creator did not define chapters.

Cited Sources

Concurring Sources

Contribution & Novelties

The lecture provides a clear synthesis of three key feedback mechanisms for self-improving AI agents, highlighting their differences and applications. It is valuable for understanding the landscape of agentic AI.

Pour aller plus loin :

  • ReAct paper — The original paper on reasoning and acting in language models.
  • RLEF paper — The paper on grounding code LLMs with execution feedback.
  • Constitutional AI paper — The paper by Anthropic on constitutional AI.
  • WebGPT — Related work on web browsing agents.
  • SWE-bench — Benchmark for evaluating code generation agents.

87 words

Radar Profile

The radar profile shows high scores in quantity and quality of information, with a slightly lower score in technical depth, indicating a well-rounded but not overly technical lecture. The overall reliability is high, reflecting the authoritative source.

Reliability 8/10