交互式训练：基于反馈的神经网络优化

摘要

传统神经网络训练通常遵循固定、预定义的优化方案，缺乏动态应对不稳定或训练中出现问题的灵活性。本文提出交互式训练（Interactive Training），这是一个开源框架，允许人类专家或自动化AI代理在神经网络训练过程中进行实时、反馈驱动的干预。其核心在于使用控制服务器来协调用户或代理与正在进行的训练过程之间的通信，使用户能够动态调整优化器超参数、训练数据和模型检查点。通过三个案例研究，我们展示了交互式训练在提升训练稳定性、降低对初始超参数的敏感性以及增强对用户需求变化的适应性方面的优势，为未来训练范式铺平了道路，即AI代理能够自主监控训练日志、主动解决不稳定性并优化训练动态。

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.

交互式训练：基于反馈的神经网络优化

Interactive Training: Feedback-Driven Neural Network Optimization

摘要

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