Trajectory 解决了 AI 产品部署后无法从用户反馈中持续学习的痛点，做 AI 产品落地的团队可以直接关注这个平台，看看如何利用用户纠错来提升模型能力。
Trajectory 是一家由前 DeepMind、OpenAI 和 Meta 超级智能研究员创立的初创公司，近日推出了一个持续学习平台，并获得了 1500 万美元融资。该平台旨在解决当前 AI 产品“冻结软件”的问题——用户每天都在纠正模型错误，但这些纠正很少被用来更新模型。Trajectory 的核心单元是“轨迹”，它结合了智能体的操作和用户的接受、拒绝、编辑、重试或修复行为，使公司能够基于完整的失败链进行训练，同时改进模型权重、提示词和智能体工作流。持续学习被认为是 AI 的下一个重大飞跃，能让模型在部署后从实际使用中不断改进。
Cracking continual learning would make AI far more…
Cracking continual learning would make AI far more capable, because models could improve from real usage after deployment.
Trajectory just launched a continual learning platform, backed by a $15M round, to turn every agent trace and user correction into a system that keeps improving after deployment.
A neolab with ex-DeepMind, OpenAI, and Meta Superintelligence researchers that also has paying customers, totally normal.
AI products are still frozen software, because users correct them every day but those corrections rarely update the model, the prompts, or the surrounding agent workflow.
Trajectory’s core unit is the trajectory, which combines what the agent did with what the user accepted, rejected, edited, retried, or fixed later, so companies can train on full failure chains and improve model weights, harness, and prompts together.
The next major AI leap almost certainly will come from models that keep learning after they are shipped.