让训练过程可交互，实时调整参数提升稳定性
Interactive Training: Feedback-Driven Neural Network Optimization
- 通过控制服务器实现人或AI实时干预训练过程
- 显著降低对初始超参数的敏感性，提升训练稳定性
- 适合需要动态调参的研究者和自动化训练系统
传统神经网络训练依赖固定优化策略，难以应对训练中的突发问题。本文提出Interactive Training——一个开源框架，支持人类专家或自动化AI代理在训练过程中实时反馈干预。核心是控制服务器，协调用户或智能体与训练进程的通信，可动态调整优化器超参数、训练数据和模型检查点。三个案例研究显示，该方法显著提升训练稳定性，降低对初始超参数的敏感性，并增强对用户需求变化的适应能力，为未来由AI自主监控日志、主动修复不稳定状态并优化训练动态的新型训练范式铺平道路。
原文摘要 · Abstract (English)
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.
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