fast-slow-training

This tweet announces Fast-Slow Training (FST), a new continual learning method that treats model parameters as slow weights and optimized context as fast weights, reportedly outperforming weights-only training on math, code, and general reasoning benchmarks.

Fast-Slow Training (FST) interleaves context optimization (via GEPA) with model weight updates via RL, achieving 3× sample efficiency over RL alone on math, code, and physics reasoning while preserving plasticity and enabling continual learning.