Stanford CS25: Transformers United V6 I Distinct Modes of Generalization from Parameters and Context @stanfordonline
Stanford CS25: Transformers United V6 I Distinct Modes of Generalization from Parameters and Context  @stanfordonline
Uploaded May 2026 | Updated September 2026, 2 weeks ago
For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

May 7, 2026
This seminar covers:
• Two methods for teaching information to language models: training (updating parameters) or in-context learning (providing information in prompts)
• Striking differences in the types of generalization that models make when they learn information via these two routes
• Three different strategies that can help bridge the gap, based on data augmentation, retrieval, and RL

Follow along with the seminar schedule. Visit: https://web.stanford.edu/class/cs25/

Guest Speaker: Andrew Lampinen (Anthropic)

Instructors:
• Steven Feng, Stanford Computer Science PhD student and NSERC PGS-D scholar
• Karan P. Singh, Electrical Engineering PhD student and NSF Graduate Research Fellow in the Stanford Translational AI Lab
• Michael C. Frank, Benjamin Scott Crocker Professor of Human Biology Director, Symbolic Systems Program
• Christopher Manning, Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science, Co-Founder and Senior Fellow of the Stanford Institute for Human-Centered Artificial Intelligence (HAI)
Stanford CS25: Transformers United V6 I Distinct Modes of Generalization from Parameters and ContextStanford CS229 Machine Learning | Spring 2026 | Lecture 11: Diffusion ModelsStanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 3: Calculus of VariationsLecture 15 - PCA and ICA | Stanford CS229: Machine Learning Andrew Ng - Autumn 2018Stanford CS25: Transformers United V6 I From Next-Token Prediction to Next-Generation IntelligenceStanford CS221 | Autumn 2025 | Lecture 12: Bayesian Networks IStanford CS221 | Autumn 2025 | Lecture 5: Search IStanford CS229 Machine Learning | Spring 2026 | Lecture 7: Neural Networks 1 (Architecture)Stanford CS153 Frontier Systems | The Road Ahead: Resilience RequiredNew Stanford Course: Agentic AIOverview: Business Opportunities and Applications of Generative AIStanford CS229 Machine Learning | Spring 2026 | Lecture 16: Basic Concept in RL, Policy Gradient
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Stanford CS25: Transformers United V6 I Distinct Modes of Generalization from Parameters and Context

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