Originally published at rewire.it
TL;DR: Google's Nested Learning framework reveals neural networks as hierarchies of optimization problems operating at different timescales. Their HOPE architecture maintains 65-75% accuracy at 1M tokens while standard models collapse. This post explains the theory, biology, and gives you working implementation code.
Your language model experiences each conversation as if it's the first. Once training ends, it can't form new memories—only process what fits in its context window. Feed it new information, and it either forgets when context clears, or you retrain from scratch and watch it catastrophically overwrite everything it knew.
This isn't a bug. It's a fundamental limitation of how we've built deep learning systems.
What if I told you the problem isn't the architecture itself, but that we've been thinking about learning all wrong? What if neural networks aren't really about stacking layers, but about compressing information at multiple timescales—just like your brain does?
What You'll Learn
In this deep-dive, we'll explore Nested Learning, a framework from Google Research that fundamentally reframes how neural networks learn:
- 🧠 Why optimizers are actually memory systems (and what that means for training)
- ⚡ How different network components should update at different frequencies (not just "one learning rate for everything")
- 🔬 The biological inspiration: brain oscillations and memory consolidation
- 💻 Practical implementation: HOPE architecture with working Python code
- 📊 Real results: 65-75% accuracy at 1 million tokens (vs 0% for most models)
The Core Insight
Modern transformers update all parameters at a single frequency during training. Your brain doesn't work this way. When you learn something:
- Fast consolidation happens in minutes-hours (hippocampus)
- Slow consolidation happens over days-weeks (cortex)
- Different brain waves (delta, theta, gamma) coordinate different timescales
Nested Learning brings this multi-timescale approach to artificial neural networks—explicitly. The result? Models that can actually learn continuously without catastrophic forgetting.
Who This Is For
This post is for ML engineers and researchers who:
- Want to understand why continual learning is so hard
- Are frustrated by catastrophic forgetting in fine-tuning
- Need to work with extremely long contexts (100K+ tokens)
- Want practical implementation guidance (not just theory)
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