交互式训练2：用于实时模型训练的可审计控制平面

摘要

实验跟踪器能够展示训练的进展，但调整实时训练流程通常仍需要训练器专属代码。我们提出Interactive Training 2，这是一个通过共享协议引导训练的开源控制平面。训练应用声明其暴露的设置和操作，人类与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执行这些请求。定制化的Aim工作区将实时指标和控件与请求及结果的按时间顺序记录相结合。我们在五个自然语言处理和强化学习工作流程中演示了该系统。发布的代码和轨迹为可审计的人工与智能体引导训练提供了可复用的基础。

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.