NatureNLP Progress Timeline
Initial oscillatory experiments
First experiments with oscillatory gating mechanisms on GPT-2 base models. Exploring sparse activation patterns.
Oscillatory GPT-2 with improved gating
Refined oscillatory mechanisms with improved gating functions. Better efficiency metrics compared to baseline.
Multi-task fine-tuned model
Multi-task fine-tuning approach with enhanced performance. Improved efficiency through better training strategies.
Active research and experimentation
Ongoing development of prototypes demonstrating oscillatory architectures, training-time efficiency optimizations, and inference-time improvements.
Documentation of approach and vision
Created comprehensive pitch deck outlining the efficiency-first approach, nature-inspired principles, and roadmap for adoption by larger models.
Benchmarking, ablations, and deployment
Upcoming work includes establishing baseline efficiency metrics, running ablation studies, conducting efficiency tests, and building deployment demos showcasing reduced compute requirements.