Nested Learning: Implementation from Scratch

Less Code, More Reproduction — a LeCoder project
Built to learn Google Research's Nested Learning paper end-to-end and invite others—researchers, developers, product folks, and the simply curious—to explore, fork, and improve together.

Paper & Blog:

• 📄 PDF: https://abehrouz.github.io/files/NL.pdf
• 📝 Blog: https://research.google/blog/introducing-nested-learning-a-new-ml-paradigm-for-continual-learning/

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🛠️ Built with LeCoder cGPU

This project was developed and tested using LeCoder cGPU CLI (GitHub)—a production-grade command-line tool for seamless Google Colab GPU access.

Why LeCoder cGPU?
While building this implementation, we needed a robust way to:

• Run experiments on A100 GPUs without leaving the terminal
• Manage multiple Colab sessions for parallel experiments
• Automate workflows with structured JSON output
• Integrate GPU training into our development workflow

What we built:
LeCoder cGPU provides enterprise-grade features including:

• 🔐 Secure OAuth2 authentication
• 📓 Notebook management via Drive API
• 🚀 Remote code execution with kernel mode
• 📊 Execution history and monitoring
• 🔄 Multi-session support (Colab Pro)
• 📁 File transfer and synchronization
• 🤖 AI agent integration (JSON output)

See it in action:
Check out our Enterprise Experiment Guide to see how we used LeCoder cGPU to run A100-accelerated training experiments with custom CUDA kernels.

Try it yourself:

# Install from npm (published package)
npm install -g lecoder-cgpu

# Authenticate
lecoder-cgpu auth

# Run experiment
./run_lecoder_experiment.sh full

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🎯 What is Nested Learning?

In plain English: Nested Learning is a new way of thinking about deep learning models. Instead of viewing neural networks as fixed architectures, it treats them as nested optimization problems where different parts update at different speeds—like having fast, short-term memory and slow, long-term memory working together.

For researchers: Nested Learning (NL) views models as nested, multi-level optimization problems, each with its own "context flow" and update frequency. Key insights:

• Optimizers as associative memories: Adam, SGD with momentum compress gradients into memory.
• Uniform architecture: Feedforward networks with different update clocks.
• Pre-training as in-context learning over long contexts.
• Continuum Memory System (CMS): Spectrum of fast/slow memories for long-/short-term storage.

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🚀 Quick Start

Option 1: Docker (Easiest)

# Clone the repository
git clone https://github.com/aryateja2106/nested-learning.git
cd nested-learning

# Run the interactive demo
docker compose up
# Opens at http://localhost:7860

Option 2: UV (Recommended for Development)

# Install uv if needed
curl -LsSf https://astral.sh/uv/install.sh | sh

# Setup environment
uv venv .venv && source .venv/bin/activate
UV_PYTHON=.venv/bin/python uv pip install --python .venv/bin/python -r requirements.txt

# Run tests
uv run pytest tests/test_components.py

# Launch demo
uv run python demo/app.py

# Train a small model
uv run python train_hope.py --config small --steps 500 --batch-size 8

Option 3: Standard pip

python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
python demo/app.py

📓 Try the Notebook

Open notebooks/quickstart.ipynb in Jupyter or upload to Google Colab. It runs a quick sanity check in under 2 minutes (works on CPU, faster on GPU).

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🏗️ Architecture Overview (HOPE)

┌─────────────────────────────────────────────────────────────┐
│                    Hope Architecture                         │
├─────────────────────────────────────────────────────────────┤
│  ┌─────────────────────────────────────────────────────┐   │
│  │           Self-Modifying Titans                      │   │
│  │  ┌──────────┐  ┌──────────┐  ┌──────────┐           │   │
│  │  │  M_key   │  │  M_value │  │ M_memory │           │   │
│  │  │ (adapt)  │  │ (adapt)  │  │  (adapt) │           │   │
│  │  └──────────┘  └──────────┘  └──────────┘           │   │
│  │         ↓ Delta Gradient Descent (DGD)               │   │
│  └─────────────────────────────────────────────────────┘   │
│                           ↓                                 │
│  ┌─────────────────────────────────────────────────────┐   │
│  │         Continuum Memory System (CMS)                │   │
│  │  ┌─────────┐  ┌─────────┐  ┌─────────┐  ┌─────────┐ │   │
│  │  │ MLP^f1  │→ │ MLP^f2  │→ │ MLP^f3  │→ │ MLP^fk  │ │   │
│  │  │ (high)  │  │  (mid)  │  │  (low)  │  │ (lowest)│ │   │
│  │  └─────────┘  └─────────┘  └─────────┘  └─────────┘ │   │
│  └─────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────┘

Hope combines Self-Modifying Titans (adaptive memory that learns how to learn) with Continuum Memory System (multi-frequency memory blocks).

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📚 Key Components

• Delta Gradient Descent (DGD): Updates weights with adaptive decay tied to current input.
  (W_{t+1} = W_t (I - η'_t x_t x_t^T) - η'_t ∇_y L(W_t; x_t) ⊗ x_t)
• Continuum Memory System (CMS): Spectrum of MLP blocks with different update frequencies (fast ↔ slow).
• Multi-scale Momentum Muon (M3): Fast + slow momentum with Newton-Schulz orthogonalization.
• Self-Modifying Titans: Generates and updates its own memory values—meta-learning at inference time.

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🔬 For Researchers & Developers

Training Presets

# Quick test (runs in minutes)
uv run python train_hope.py --config small  --steps 500  --optimizer adamw

# Balanced run
uv run python train_hope.py --config medium --steps 2000 --optimizer m3

# Full training (GPU recommended)
uv run python train_hope.py --config large  --steps 5000 --optimizer dgd

Using Components

from src.core.optimizers import DeltaGradientDescent, M3Optimizer
from src.core.memory import ContinuumMemorySystem
from src.models.hope import Hope, HopeConfig

# Create model
config = HopeConfig(
    d_model=512,
    num_layers=6,
    cms_num_levels=4,
    cms_base_chunk_size=16
)
model = Hope(config)

# Use novel optimizers
optimizer = M3Optimizer(model.parameters(), lr=1e-4)

☁️ Colab GPU via LeCoder cGPU CLI

Production-grade CLI for Colab GPU access—built alongside this project to enable seamless GPU-accelerated development.

Quick Start

# Install LeCoder cGPU CLI from source
cd lecoder-cgpu && npm install && npm link && cd ..

# Authenticate
lecoder-cgpu auth

# Run enterprise experiment (A100 optimized)
./run_lecoder_experiment.sh train a100 1000

Complete Workflow Example

# 1. Check authentication and GPU availability
lecoder-cgpu status --json

# 2. Create experiment notebook
lecoder-cgpu notebook create "HOPE-Experiment" --template gpu

# 3. Upload project files
lecoder-cgpu copy ./src /content/nested-learning/src

# 4. Run training with structured output
lecoder-cgpu run --json --mode kernel "
import torch
from src.models.hope import Hope, HopeConfig
# ... your code ...
"

# 5. Monitor execution history
lecoder-cgpu logs --stats

# 6. Check GPU utilization
lecoder-cgpu status --json

Enterprise Experiment Suite

Run the complete enterprise continual learning pipeline:

# Quick GPU test and benchmark
./run_lecoder_experiment.sh quick

# Full training experiment (A100 optimized)
./run_lecoder_experiment.sh train a100 1000

# CUDA performance benchmark
./run_lecoder_experiment.sh benchmark

# Complete workflow showcase
./run_lecoder_experiment.sh full

Multi-Session Management (Colab Pro)

# List active sessions
lecoder-cgpu sessions list --stats

# Run parallel experiments
lecoder-cgpu --session <id1> run "python exp1.py"
lecoder-cgpu --session <id2> run "python exp2.py"

# Switch between sessions
lecoder-cgpu sessions switch <session-id>

JSON Output for Automation

# Get structured results for AI agents
lecoder-cgpu run --json --mode kernel "your_code_here"

# Query execution history
lecoder-cgpu logs --stats --json

# Monitor runtime status
lecoder-cgpu status --json

📚 Full Documentation: See LeCoder cGPU Integration Guide for complete workflow, benchmarks, and best practices.

Tested on: A100 (Colab Pro+), T4 (Colab Pro), L4 (Free tier). Small configs run on CPU.

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💼 Enterprise Use Case: Continual Learning Pipeline

Real-world Application: Customer Intelligence System

This implementation includes a complete enterprise use case demonstrating how HOPE enables continual learning for business applications.

Business Problem

Traditional ML models suffer from catastrophic forgetting—when learning new patterns, they forget previous knowledge. This is critical for:

• Customer support systems that need to remember previous interactions
• Market analysis tools that adapt to changing conditions
• Enterprise AI that learns continuously without expensive retraining

Our Solution

The Enterprise Continual Learning Pipeline (src/experiments/enterprise_pipeline.py) demonstrates:

1. Long-term Memory: CMS maintains customer pattern memory across different update frequencies
2. Real-time Adaptation: Self-Modifying Titans adapt to new feedback patterns instantly
3. No Catastrophic Forgetting: DGD optimizer prevents knowledge loss when learning new segments

Performance Benchmarks (A100)

Metric	CPU	A100	Speedup
Training throughput	~50 tokens/s	~5,000 tokens/s	100x
Memory update latency	~10ms	~0.1ms	100x
Full training (1000 steps)	~2 hours	~2 minutes	60x

Run Enterprise Experiment

# A100-optimized training with CUDA acceleration
python -m src.experiments.enterprise_pipeline --config a100 --steps 1000

# Or via LeCoder cGPU CLI
./run_lecoder_experiment.sh train a100 1000

See: Enterprise Experiment Guide for complete documentation.

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📂 Project Structure

src/
  core/              # optimizers (DGD, M3), CMS
  models/            # Titans, Hope architecture
  experiments/       # enterprise pipeline, CUDA kernels
train_hope.py        # training entrypoint with presets (AMP on)
demo/app.py          # Gradio interactive demo
tests/               # unit tests
notebooks/           # quickstart notebook
docs/
  ALGORITHMS.md      # algorithm notes
  LECODER_CGPU_GUIDE.md  # LeCoder cGPU integration guide
run_lecoder_experiment.sh  # enterprise experiment runner
requirements.txt

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🎁 Bonus: The Skill That Built This

This implementation was created using the LeCoder Paper-to-Code Skill—a methodology for AI agents to systematically implement research papers from scratch.

Want to use it? The skill is available in .claude/skills/paper-to-code/. Download it as a ZIP and upload to Claude or other AI agents to implement your own papers.

What's included:

• Complete workflow: PDF → Markdown → Algorithm → Code → Test
• Deep-dive guides on paper analysis and implementation patterns
• Best practices for packaging and testing

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🤝 Contributing

• Fork, open issues/PRs, or share logs/results—all backgrounds welcome.
• Keep PRs small and include pytest output when touching code paths.
• Curious how this was built? The skill that created this is included—check .claude/skills/paper-to-code/.

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🙏 Acknowledgments

• Research: "Nested Learning: The Illusion of Deep Learning Architecture" (Behrouz, Razaviyayn, Zhong, Mirrokni).
• Blog: Google Research introduction (link above).
• Tools:
  • LeCoder cGPU CLI - Production-grade CLI for Colab GPU access (built alongside this project)
  • cgpu - Original inspiration for Colab-from-terminal access
• Inspiration: Open-source efforts that make cutting-edge research runnable and teachable.

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🔒 Security Note

Past commits contained credentials that are now removed; rotate/regenerate any exposed keys. .gitignore excludes common secret patterns—please keep secrets out of the repo.

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📜 Citation

@inproceedings{behrouz2025nested,
  title={Nested Learning: The Illusion of Deep Learning Architecture},
  author={Behrouz, Ali and Razaviyayn, Meisam and Zhong, Peilin and Mirrokni, Vahab},
  booktitle={NeurIPS},
  year={2025}
}

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