互動式訓練：基於反饋的神經網絡優化

摘要

傳統神經網絡訓練通常遵循固定且預先定義的優化方案，缺乏動態應對不穩定性或新出現訓練問題的靈活性。本文介紹了交互式訓練，這是一個開源框架，允許人類專家或自動化AI代理在神經網絡訓練過程中進行實時、反饋驅動的干預。交互式訓練的核心是使用控制服務器來調節用戶或代理與正在進行的訓練過程之間的通信，使用戶能夠動態調整優化器超參數、訓練數據和模型檢查點。通過三個案例研究，我們展示了交互式訓練在實現更高的訓練穩定性、降低對初始超參數的敏感性以及提高對不斷變化的用戶需求的適應性方面的優勢，為未來AI代理自主監控訓練日誌、主動解決不穩定性並優化訓練動態的訓練範式鋪平了道路。

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.

互動式訓練：基於反饋的神經網絡優化

Interactive Training: Feedback-Driven Neural Network Optimization

摘要

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