Applied Compute Training

Applied Compute Training is an AI tool designed to fine-tune machine learning models optimally by allowing users to start with an existing model and enhance performance via iterative experiments. Utilizing a fully asynchronous Reinforcement Learning (RL) system, high-throughput inference, and lower-precision training, it optimizes model efficiency and stability while supporting full parameter tuning for multi-trillion parameter models without requiring cluster management. Incorporating Continual Learning with self-distillation and an Applied Research Intelligence agent named 'Ari', the tool enables models to improve continuously from production traces while automating research loops, monitoring runs, fixing configurations, and generating reports within existing framework setups.