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    <abstract>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 resolves instabilities, and optimizes training dynamics.</abstract>
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%0 Conference Proceedings
%T Interactive Training: Feedback-Driven Neural Network Optimization
%A Zhang, Wentao
%A Lu, Yang Young
%A Deng, Yuntian
%Y Habernal, Ivan
%Y Schulam, Peter
%Y Tiedemann, Jörg
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-334-0
%F zhang-etal-2025-interactive
%X 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 resolves instabilities, and optimizes training dynamics.
%U https://aclanthology.org/2025.emnlp-demos.65/
%P 851-861

Markdown (Informal)

[Interactive Training: Feedback-Driven Neural Network Optimization](https://aclanthology.org/2025.emnlp-demos.65/) (Zhang et al., EMNLP 2025)

• Interactive Training: Feedback-Driven Neural Network Optimization (Zhang et al., EMNLP 2025)

ACL

• Wentao Zhang, Yang Young Lu, and Yuntian Deng. 2025. Interactive Training: Feedback-Driven Neural Network Optimization. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 851–861, Suzhou, China. Association for Computational Linguistics.