Inflation-Inspired Optimization
Explore advanced optimization techniques inspired by inflation cosmology for enhanced learning dynamics.
Theoretical Modeling
Construct differential geometric frameworks linking inflation parameters to optimization dynamics.
Algorithm Experiment
Design inflation-inspired optimizers using GPT-4 fine-tuning for improved learning rates.
Validation Process
Compare proposed optimizers with standard methods, measuring perplexity and training stability.
Why GPT-4 Fine-Tuning?
Advanced Math: Inflation models require tensor perturbations and nonlinear dynamics, beyond GPT-3.5’s capabilities.
Long Context: Spacetime-scale analysis needs >16k tokens, feasible only with GPT-4’s extended context.
Controlled Experiments: Fine-tuning enables physics-informed constraints (e.g., energy conservation), unlike GPT-3.5’s generic tasks.
GPT-3.5’s Limits: Its API lacks support for complex symbolic reasoning and multi-field coupling simulations.
Inflation Optimizer
Exploring novel optimization techniques inspired by inflation cosmology dynamics.