Interactive Training: Feedback-Driven Neural Network Optimization
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.
68df29e3df49fb0df1e03bca
Wentao Zhang
68df29e3df49fb0df1e03bcb
Yang Young Lu
68df29e3df49fb0df1e03bcc
Yuntian Deng
claimed_verified
2025-10-03T12:33:08.482Z
63081e15a670ed10f9d44229
https://cdn-avatars.huggingface.co/v1/production/uploads/63081e15a670ed10f9d44229/w1b9uq-9774bMMgJbSPsS.jpeg
Yuntian Deng
user
yuntian-deng
Interactive Training: Feedback-Driven Neural Network Optimization
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.
Interactive Training is a framework that allows real-time, feedback-driven intervention during neural network training, improving stability and adaptability.
Interactive Training control server optimizer hyperparameters training data model checkpoints training stability sensitivity to initial hyperparameters adaptability AI agents training logs training dynamics