让训练过程可交互、可审计，支持人和自动化系统实时调控。
Interactive Training 2: Auditable Control Plane for Live Model Training
- 通过统一接口实现人类与自动控制器对训练过程的实时干预。
- 在安全控制点验证并应用请求，确保训练稳定可靠。
- 适用于NLP和强化学习任务，支持可追溯的训练记录。
实验追踪工具能显示训练进展，但修改正在进行的训练通常仍需特定代码。我们提出Interactive Training 2，一个开源的控制平面，通过共享协议实现对训练过程的交互式引导。训练应用声明可暴露的设置与操作，人类和自动化控制器通过同一接口提交请求，训练循环在安全控制点验证并应用这些请求。定制化的Aim工作区将实时指标与控制界面结合，同时记录请求与结果的时间序列。我们在五个NLP和强化学习工作流中演示了该系统。发布的代码与训练轨迹为可审计的人类及智能体引导训练提供了可复用的基础。
原文摘要 · Abstract (English)
Experiment trackers show how training is progressing, but changing a live run still usually requires trainer-specific code. We present Interactive Training 2, an open-source control plane for steering training through a shared protocol. Training applications declare which settings and actions they expose, humans and automated controllers submit requests through the same interface, and the training loop validates and applies them at safe control points. A customized Aim workspace combines live metrics and controls with a chronological record of requests and outcomes. We demonstrate the system across five NLP and reinforcement-learning workflows. The released code and traces provide a reusable foundation for auditable human- and agent-guided training.
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