Stanford OnlineStanford CS329A Self-Improving AI Agents | Part 1 | Course Overview
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At a glance
WHAT IT’S REALLY ABOUT
CS329A overview: scaling LLMs, reasoning, inference scaling, and agents
- The instructors introduce CS329A as a course focused on self-improving AI agents and outline how it will connect large language model foundations to modern agentic workflows.
- The lecture reviews how scaling compute, data, and parameters historically improved language model performance and enabled few-shot and emergent reasoning behaviors.
- The instructors explain the post-training stack—higher-quality fine-tuning, instruction tuning, and RLHF—as key steps that shaped ChatGPT-like assistants beyond raw pre-training.
- They highlight inference-time/test-time scaling methods such as repeated sampling with verifiers, arguing that fixed models can solve harder tasks by generating many candidates and selecting good ones.
- They frame the current frontier as agentic systems that plan, call tools, incorporate verifiers/judges, and self-correct to accomplish end-to-end real-world tasks, then close with course logistics, grading, and project expectations.
IDEAS WORTH REMEMBERING
5 ideasScaling laws (compute/data/parameters) drove major capability gains—until signs of saturation.
Dr. Aakanksha Chowdhery describes how increasing compute, dataset size, and parameter count historically reduced test loss and improved benchmark performance, which underpinned the rapid growth in model sizes from 2018 through 2024 before beginning to show signs of saturation.
Few-shot and chain-of-thought behaviors enable models to generalize from prompts, especially as models get larger.
Dr. Aakanksha Chowdhery explains zero-shot and few-shot prompting, and Azalia Mirhoseini explains that chain-of-thought prompting can improve performance by including intermediate reasoning steps; she also notes this benefit tends to appear more clearly at larger model sizes.
Reasoning-model gains come from learned thinking skills like decomposition and self-correction, not just bigger models.
Azalia Mirhoseini attributes much recent progress in “thinking” models to chain-of-thought and related behaviors (analysis, decomposition, backtracking/self-correction), describing them as skills models learn and generalize through training and optimization processes.
Inference-time scaling plus verification can unlock capabilities that single-shot prompting leaves unused.
Azalia Mirhoseini presents repeated sampling with a verifier (their “Large Language Monkeys” work): generating many candidate solutions and selecting a correct one can make smaller open models outperform a stronger model that is sampled only once, highlighting variance and the value of selection.
Post-training (instruction tuning + RLHF) steers raw pre-trained models into usable assistants aligned with preferences.
The instructors outline the standard pipeline from pre-training to higher-quality fine-tuning, instruction tuning, and RLHF, emphasizing that RLHF uses human preference data to build reward models that steer generations toward desired qualities like helpfulness or harmlessness.
WORDS WORTH SAVING
5 quotesSo one of the aspects that has been mind-blowing since, uh, GPT-3 came out is that, uh, there is a, uh... that as you scale up the number of parameters in a model, uh, they get better.
— Dr. Aakanksha Chowdhery
There are new kind of behaviors that appear in the model that they didn't have before, and we never could predict that until we had these bigger models and saw these kind of behavior in them.
— Azalia Mirhoseini
It kind of seems like the model or models already know a whole lot more than what you get out of them when you just ask them once, right?
— Azalia Mirhoseini
So the reason it's called inference scaling is that we are not touching any of the parameters of the model. The model is fixed, and we are just like at inference time, we are creating different ways to generate, to kind of produce generations from the model and use them to generate higher quality answers or answers to harder problems for the model.
— Azalia Mirhoseini
Large language models as chatbots or as, uh, reasoning models are basically still single turn, like, or, or just, like, in the chat format. So they're not accomplishing a task for you.
— Dr. Aakanksha Chowdhery
SPEAKERS
Dr. Aakanksha Chowdhery
hostAdjunct Professor at Stanford and researcher at Reflection AI focusing on large language models and self-improving AI agents.
Azalia Mirhoseini
hostStanford CS faculty member and AI researcher with experience at Google Brain/DeepMind and work related to Claude (Anthropic) and Gemini.
High quality AI-generated summary created from speaker-labeled transcript.
