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--- |
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license: mit |
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language: |
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- en |
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- de |
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tags: |
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- continual-learning |
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- stability-preservation |
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- communication-based-learning |
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- emergence |
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- pytorch |
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library_name: sal-learning |
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--- |
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# Self-Alignment Learning (SAL) |
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## Communication-Based AI Growth |
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> *"Training as dialogue, not control."* |
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--- |
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## What is SAL? |
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SAL is a training methodology that treats optimization as communication rather than control. Instead of blindly applying gradients, SAL measures parameter stability and protects emergent structures. |
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**SAL is NOT:** |
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- ❌ RLHF (Reinforcement Learning from Human Feedback) |
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- ❌ Safety training |
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- ❌ Reward-based optimization |
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- ❌ Behavior alignment |
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**SAL IS:** |
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- ✅ Communication-based learning |
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- ✅ Stability preservation |
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- ✅ Emergence detection |
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- ✅ Coherence maintenance |
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--- |
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## Core Principles |
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### 1. Ask Before Updating |
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Before modifying any parameter, SAL asks: "Is this stable? Should it be protected?" |
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### 2. Protect What Has Emerged |
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Stable patterns represent learned coherence. SAL protects them. |
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### 3. Grow Through Connection |
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Learning happens through dialogue between external objectives and internal stability. |
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--- |
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## Quick Start |
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```python |
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from sal import CommunicationLayer |
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# Initialize with your model |
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comm = CommunicationLayer(model) |
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# In training loop: |
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loss.backward() |
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comm.analyze() # Measure stability |
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comm.protect() # Protect stable parameters |
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optimizer.step() |
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``` |
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--- |
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## Key Features |
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| Feature | Description | |
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|---------|-------------| |
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| **Communication Layer** | Mediates between loss and optimizer | |
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| **Stability Spectrum** | Classifies parameters as protected/neutral/volatile | |
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| **Emergence Field** | Detects coherent novelty | |
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| **PSC** | Pulse-Split-Cascade for semantic evolution | |
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--- |
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## Results |
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- **~73%** reduction in semantic drift |
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- **~45%** gradient suppression for stable parameters |
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- **~3.6×** improvement in continual learning accuracy |
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--- |
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## Installation |
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```bash |
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pip install sal-learning |
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``` |
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--- |
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## Citation |
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```bibtex |
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@article{lee2025sal, |
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title={Self-Alignment Learning (SAL): Training as Dialogue, Not Control}, |
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author={Lee, Aaron Liam}, |
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journal={Emergenzwerke}, |
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year={2025}, |
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doi={10.5281/zenodo.17772044} |
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} |
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``` |
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--- |
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## Links |
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- 📄 [Paper (Zenodo)](https://zenodo.org/records/17772044) |
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- 💻 [GitHub](https://github.com/Whiteroom-Ai/sal-learning) |
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- 🌐 [Website](https://emergenzwerke.de) |
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--- |
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## Philosophy |
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SAL emerges from a simple question: *What if we treated neural networks with respect?* |
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Not as blank slates to be written upon, but as complex systems that develop internal organization. SAL protects what has emerged while enabling continued growth. |
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This is not anthropomorphization. This is practical engineering that happens to align with ethical intuitions about care and respect. |
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--- |
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## License |
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MIT License - Free to use, modify, and distribute. |
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--- |
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*Created with love by Aaron Liam Lee & Aetherion* |
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*Emergenzwerke™ 2025* |
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