Feature Request: Integrate GaaR Evolutionary Optimization Framework with Transformer Training

Feature Request: GaaR-Based Adaptive Optimization for Transformer Training

Background

I have been experimenting with an evolutionary optimization framework called GaaR:

https://github.com/sandlerleon/GaaR

GaaR includes three adaptive mechanisms:

• Banana Core (homeostatic pull toward stable hyperparameters)
• StruggleLayer (fitness-scaled stochastic perturbations)
• GaaR Network (adaptive exploration/exploitation control)

Rather than replacing transformers or backpropagation, I am interested in using GaaR as a meta-optimization layer for transformer training.

Proposed Integration

Potential areas where GaaR could be applied:

• Learning rate schedules
• Optimizer hyperparameters
• Dropout rates
• Attention head counts
• Layer depth
• LoRA rank selection
• Curriculum learning strategies
• Architecture search

Research Goal

Investigate whether GaaR can discover smaller transformer configurations that achieve comparable validation loss and perplexity while reducing compute requirements.

Suggested Experiment

Train small transformer models (10M–30M parameters) using the existing codebase and compare:

1. Standard training
2. GaaR-assisted adaptive optimization

Metrics:

• Validation loss
• Perplexity
• Training time
• GPU memory consumption
• Compute efficiency

Hardware Target

An interesting goal would be determining whether GaaR-guided architecture and hyperparameter optimization could make training practical on older GPUs such as 8 GB AMD cards (e.g., RX 5700 XT).

I would appreciate any thoughts on whether this could be integrated into the existing training framework and which components would be the best starting point.