File size: 2,938 Bytes
2c914eb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 | ---
license: mit
language:
- en
- de
tags:
- continual-learning
- stability-preservation
- communication-based-learning
- emergence
- pytorch
library_name: sal-learning
---
# Self-Alignment Learning (SAL)
## Communication-Based AI Growth
> *"Training as dialogue, not control."*
---
## What is SAL?
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.
**SAL is NOT:**
- ❌ RLHF (Reinforcement Learning from Human Feedback)
- ❌ Safety training
- ❌ Reward-based optimization
- ❌ Behavior alignment
**SAL IS:**
- ✅ Communication-based learning
- ✅ Stability preservation
- ✅ Emergence detection
- ✅ Coherence maintenance
---
## Core Principles
### 1. Ask Before Updating
Before modifying any parameter, SAL asks: "Is this stable? Should it be protected?"
### 2. Protect What Has Emerged
Stable patterns represent learned coherence. SAL protects them.
### 3. Grow Through Connection
Learning happens through dialogue between external objectives and internal stability.
---
## Quick Start
```python
from sal import CommunicationLayer
# Initialize with your model
comm = CommunicationLayer(model)
# In training loop:
loss.backward()
comm.analyze() # Measure stability
comm.protect() # Protect stable parameters
optimizer.step()
```
---
## Key Features
| Feature | Description |
|---------|-------------|
| **Communication Layer** | Mediates between loss and optimizer |
| **Stability Spectrum** | Classifies parameters as protected/neutral/volatile |
| **Emergence Field** | Detects coherent novelty |
| **PSC** | Pulse-Split-Cascade for semantic evolution |
---
## Results
- **~73%** reduction in semantic drift
- **~45%** gradient suppression for stable parameters
- **~3.6×** improvement in continual learning accuracy
---
## Installation
```bash
pip install sal-learning
```
---
## Citation
```bibtex
@article{lee2025sal,
title={Self-Alignment Learning (SAL): Training as Dialogue, Not Control},
author={Lee, Aaron Liam},
journal={Emergenzwerke},
year={2025},
doi={10.5281/zenodo.17772044}
}
```
---
## Links
- 📄 [Paper (Zenodo)](https://zenodo.org/records/17772044)
- 💻 [GitHub](https://github.com/Whiteroom-Ai/sal-learning)
- 🌐 [Website](https://emergenzwerke.de)
---
## Philosophy
SAL emerges from a simple question: *What if we treated neural networks with respect?*
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.
This is not anthropomorphization. This is practical engineering that happens to align with ethical intuitions about care and respect.
---
## License
MIT License - Free to use, modify, and distribute.
---
*Created with love by Aaron Liam Lee & Aetherion*
*Emergenzwerke™ 2025*
|