Feature Request: Add NEAT (Nash-Equilibrium Adaptive Training) Optimizer #2883
Description
Description:
Neural network optimization for billion-parameter models faces critical gradient conflict issues where parameter updates across different layers interfere destructively, leading to slower convergence, higher variance, and resource inefficiency. NEAT (Nash-Equilibrium Adaptive Training) addresses this by modeling neural network optimization as a multi-agent game governed by Nash equilibrium principles, treating each layer as a rational agent. This game-theoretic optimizer achieves significantly faster convergence, improved stability, and substantial resource and environmental savings.
Key Contributions (from 2025 TJAS research paper by Goutham Ronanki):
- Nash Gradient Equilibrium (NGE): Each layer acts as a rational player; gradients are projected onto the Nash equilibrium manifold using the network's graph Laplacian, reducing destructive gradient interference.
- NG-Adam: Integrates NGE with Adam by adding equilibrium correction to momentum estimation.
- Nash Step Allocation (NSA): Layerwise adaptive learning rates increase for well-aligned gradients, decrease for high-conflict layers.
- Empirical Results:
- 28% faster convergence (32,400 vs. 45,000 steps; Adam baseline).
- 20% reduction in GPU hours, with proportional cost and carbon savings (8–10 metric tons CO₂/run).
- Dramatic reduction in layer gradient conflicts (mean cosine similarity: Adam -0.12 → NEAT +0.08).
- Consistent benefits scale with larger models (improvement grows from 16% @50m to 31% @1.2B params).
- All results statistically significant (p < 0.001, Cohen's d > 0.8).
Algorithmic Sketch (from paper Appendix):
# NEAT Nash-Equilibrium Adaptive Training
for batch in training_data:
G = compute_gradients(model, batch)
L = graph_laplacian(model_structure)
G_equil = (I - mu * L) @ G
m = beta1 * m + (1 - beta1) * G_equil
v = beta2 * v + (1 - beta2) * (G_equil ** 2)
eta_i = eta / (1 + ||L G_i||) # Nash Step Allocation
param -= eta_i * m / (sqrt(v) + eps)Implementation Plan:
- tf.keras native optimizer integrating NGE, NG-Adam, and NSA
- Laplacian construction for neural architectures
- Full usage/benchmark notebooks
- Empirical validation pipeline on open datasets (text, vision)
References:
- Ronanki, G. Nash-Equilibrium Adaptive Training (NEAT). TJAS, 2025 (full PDF attached, see GitHub)
- https://github.com/ItCodinTime/neat-optimizer
Theoretical background, further results, and step-by-step algorithmic descriptions are included in the attached PDF (see repo). Please review and advise on desired API/interface for TF Addons inclusion.