NL.pdf In this episode, we dive into Nested Learning (NL) — a new framework that rethinks how neural networks learn, store information, and even modify themselves. While modern language models have made remarkable progress, fundamental questions remain: How do they truly memorize? How do they improve over time? And why does in-context learning emerge at scale? Nested Learning proposes a bold answer. Instead of viewing a model as a single optimization problem, NL treats it as a hierarchy...
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NL.pdf In this episode, we dive into Nested Learning (NL) — a new framework that rethinks how neural networks learn, store information, and even modify themselves. While modern language models have made remarkable progress, fundamental questions remain: How do they truly memorize? How do they improve over time? And why does in-context learning emerge at scale? Nested Learning proposes a bold answer. Instead of viewing a model as a single optimization problem, NL treats it as a hierarchy...
NL.pdf In this episode, we dive into Nested Learning (NL) — a new framework that rethinks how neural networks learn, store information, and even modify themselves. While modern language models have made remarkable progress, fundamental questions remain: How do they truly memorize? How do they improve over time? And why does in-context learning emerge at scale? Nested Learning proposes a bold answer. Instead of viewing a model as a single optimization problem, NL treats it as a hierarchy...
https://arxiv.org/pdf/2511.10395 What if AI agents could teach themselves? In this episode, we dive into AgentEvolver, a groundbreaking framework from Alibaba's Tongyi Lab that flips the script on how we train autonomous AI agents. Traditional agent training is brutal: you need manually crafted datasets, expensive random exploration, and mountains of compute. AgentEvolver introduces a self-evolving system with three elegant mechanisms that let the LLM drive its own learning: Self-Questioning ...
NL.pdf In this episode, we dive into Nested Learning (NL) — a new framework that rethinks how neural networks learn, store information, and even modify themselves. While modern language models have made remarkable progress, fundamental questions remain: How do they truly memorize? How do they improve over time? And why does in-context learning emerge at scale? Nested Learning proposes a bold answer. Instead of viewing a model as a single optimization problem, NL treats it as a hierarchy...
