Interactive Training: Feedback-Driven Neural Network Optimization
Authors: name: Wentao Zhang, name: Yang Young Lu, name: Yuntian Deng
Organization: Yuntian Group

Traditional neural network training typically follows fixed, predefined
optimization recipes, lacking the flexibility to dynamically respond to
instabilities or emerging training issues. In this paper, we introduce
Interactive Training, an open-source framework that enables real-time,
feedback-driven intervention during neural network training by human experts or
automated AI agents. At its core, Interactive Training uses a control server to
mediate communication between users or agents and the ongoing training process,
allowing users to dynamically adjust optimizer hyperparameters, training data,
and model checkpoints. Through three case studies, we demonstrate that
Interactive Training achieves superior training stability, reduced sensitivity
to initial hyperparameters, and improved adaptability to evolving user needs,
paving the way toward a future training paradigm where AI agents autonomously
monitor training logs, proactively resolve instabilities, and optimize training
dynamics.