Interactive Training 2: Auditable Control Plane for Live Model Training

AI 中文解读

Interactive Training 2 来了！这篇论文带来一个开源“控制台”，让训练中的AI模型像直播一样可以随时“调音”——以前想中途改参数得自己写代码，现在通过统一界面直接提交请求，系统会在安全时机自动执行，所有操作都记录在案。简而言之，这相当于给AI训练装上了“方向盘”和行车记录仪：研究员可以边看实时数据边微调，甚至可以让外部程序自动帮忙调参，调了什么、什么时候调的、结果如何，全程可追溯。这项技术最直接的影响是让AI开发更灵活高效——比如训练客服机器人时，发现它总回答错误，不用停下重来，直接修改参数就能纠正；对普通人来说，虽然不直接写代码，但未来可能出现“训练调节器”这类工具，让非技术用户也能在AI学习过程中提出反馈，加速智能应用迭代。简单说，AI训练从“黑箱操作”变成了“可监督、可干预的透明流程”。

arXiv:2607.18314v1 Announce Type: new Abstract: 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.