Topic
Gpt 4
3 episodes
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Overview: In Context Learning
We finally slow down and explain in-context learning, the thing we keep leaning on whenever prompts, agents, examples, and adaptation come up. We make the core idea concrete: the model is learning from the temporary packet you hand it, without changing itself permanently.
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From Context to Skills: Can Language Models Learn from Context Skillfully?
Cody and Justy dig into Ctx2Skill, a self-evolving framework that turns long, dense context into reusable natural-language skills for language models. They talk through the core loop, the role of Challenger, Reasoner, Judge, and the replay trick that keeps the system from drifting into weird overfit territory, then land on what it means for product teams trying to ship context-heavy workflows.
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Contexts are Never Long Enough: Structured Reasoning for Scalable Question Answering over Long Document Sets
A new QA framework called SLIDERS handles document sets that outgrow any context window by storing extracted facts in a relational database and reasoning over them with SQL.