Interactive Training 2 Offers Auditable Control for Live AI Models

While experiment trackers are excellent for monitoring the progress of AI model training, making real-time adjustments to a live training run typically requires modifying specific code within the trainer. This process can be cumbersome, lacks standardization, and often leaves no clear audit trail of changes. To address these challenges, "Interactive Training 2" has been introduced as an open-source control plane. This system provides a standardized protocol for steering live model training. It allows training applications to explicitly declare which settings and actions can be exposed and modified. Both human operators and automated controllers can then submit requests through a unified interface. The system ensures that these requests are validated and applied safely at designated control points within the training loop. A customized Aim workspace integrates live performance metrics with a chronological record of all submitted requests and their outcomes, creating a fully auditable history of the training process. This framework has been demonstrated across various NLP and reinforcement learning workflows, offering a reusable foundation for transparent, human- and agent-guided model training.