MARS: Make vAriance Reduction Shine
A unified optimization framework that combines preconditioned gradient methods with variance reduction for efficient training of large language models.
MARS: Make vAriance Reduction Shine
A unified optimization framework that combines preconditioned gradient methods with variance reduction for efficient training of large language models.
Key Components
- • Scaled stochastic recursive momentum for variance-reduced gradient estimation
- • Preconditioned updates approximating second-order Newton&aposs method
- • Optimal γ parameter of 0.025 for gradient correction
Performance Highlights
- • Outperforms AdamW in GPT-2 training
- • Achieves 2.53 validation loss on GPT-2 large
- • 44.20% accuracy on Hellaswag vs AdamW&aposs 42.31%
Available Variants
Key Features
Competitive Environment
Train and evaluate AI agents in challenging competitive scenarios
Multi-Agent Support
Built specifically for multi-agent reinforcement learning research
Advanced Training
Sophisticated training mechanisms and evaluation metrics
How It Works
1
Define Your Agents
Create your AI agents using the MARS framework and define their behaviors and learning strategies.
2
Train in Competition
Put your agents through competitive training scenarios to improve their performance and adaptability.
3
Evaluate & Iterate
Use comprehensive metrics and analysis tools to evaluate agent performance and refine your approaches.