无论是由OpenAI还是Anthropic开发，还是建立在开源替代品上，目前的AI模型都有一个关键限制：他们不会从错误中吸取教训
企业为法律和金融等专门领域改造这些模型，通常由微调一般模型绕过。 然而，这往往需要雇用内部研究小组或依赖昂贵的顾问，通常称为前调工程师(FDEs).
Tropjectory是大约一年前由三位在Google DeepMind和Apple有经验的斯坦福同学创立的启动项目，希望实现这一过程的自动化，让AI模型从反馈中学习，并在专门任务中不断改进。
18人公司只是宣布，它以3亿美元的估值在A系列回合中以4500万美元进行，由Sequoia领导，Nvidia,Bessemer Venture Partners，和Bain Capital Ventures参与。
Ronak Malde将基础模型比作一个非常聪明的博士生，他告诉TechCrunch。
启动的目的是通过一种称为持续学习的方法来解决这个问题。 使用Trajectory的企业可以从开源 AI模型开始，并将平台与他们的应用程序连接起来，使其能够收集模型如何表现的反馈。 轨迹然后利用该信息对模型进行再培训，帮助其在随着时间的推移的专门任务中改进。
有了Traphextory，律师，医生和其他专家可以自动改进近身的模型，Malde 称。 这种能力可以使专门AI的部署更快，更便宜。
Harvey和Rogo等公司已经在使用Trajectory的平台来训练开源模型后，通过学习以前的任务，如审查特定类型的法律合同或使用特定方法对公司进行估值，帮助他们更像有经验的律师或财务分析师。 轨迹的其他客户包括Decagon,Mercor，和Nvidia. 创业者目前正在公开其培训后平台。
使用Trajectory的人决定“他们的组织是什么好样的，并且可以把它变成他们的模型的改进”,Malde 称。
轨迹学对持续学习的方法与递归自我改进(RSI)有一些相似之处，即AI系统可以提高自身的能力。 但共同创始人迈克尔·埃拉布德(Michael Elabd)认为，让人类参与培训过程使得Trajectory的方法更加安全，因为自主的自我改善可能导致意想不到甚至危险的结果。
轨迹并非唯一试图实现训练后自动化的创业者。 它考虑由前OpenAI CTO Mira Murati和微调平台的幕后公司Tinker创办的"思维机器实验室"，与上月价值32.5亿美元的Application Compute，以及Prime Intellect帮助公司建立和训练自己的AI agents.
Malde认为创业者的最大优势在于其团队。 他是少数来自Windsurf的研究人员之一。 Elabd也在DeepMind工作，而Trajectory的第三个联合创始人Arjun Karanam此前在Apple从事基因基础模型的研究。 启动还从OpenAI,Stripe，和Netflix招聘了研究人员和产品设计师。
为什么设计对一家专注于AI培训的公司如此重要？启动的Karanam 称开发了一个界面，使其非技术的客户更容易获得培训后。
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Whether developed by OpenAI or Anthropic or built on open source alternatives, current AI models share a critical limitation: They don’t learn from their mistakes without further training.
Businesses adapting these models for specialized fields, such as law and finance, typically get around this by fine-tuning general models. However, that often requires hiring internal research teams or relying on expensive consultants, commonly known as forward-deployed engineers (FDEs).
Trajectory, a startup founded about a year ago by three Stanford classmates with experience at Google DeepMind and Apple, wants to automate that process, allowing AI models to learn from feedback and continuously improve at specialized tasks.
The 18-person company just announced that it raised $45 million in a Series A round at a $300 million valuation, led by Sequoia, with participation from Nvidia, Bessemer Venture Partners, and Bain Capital Ventures.
Trajectory co-founder Ronak Malde compared foundational models to a very smart PhD student who’s always starting a new job. “But what the economy values is someone who worked on the job for 30 years,” he told TechCrunch.
The startup aims to solve this problem through an approach known as continual learning. Businesses using Trajectory can start with an open source AI model and connect the platform to their applications, allowing it to collect feedback on how the model performs. Trajectory then uses that information to retrain the model, helping it improve at specialized tasks over time.
With Trajectory, lawyers, doctors, and other specialists can improve the models near automatically themselves, Malde said. That capability could make deploying specialized AI faster and cheaper.
Companies like Harvey and Rogo are already using Trajectory’s platform to post-train open source models, helping them perform more like experienced lawyers or financial analysts by learning from previous tasks, such as reviewing a particular type of legal contract or valuing a company using a specific methodology. Trajectory’s other customers include Decagon, Mercor, and Nvidia. The startup is now making its post-training platform publicly available.
The people using Trajectory decide “what good looks like for their organization and can turn that into improvements for their model,” Malde said.
Trajectory’s approach to continual learning shares some similarities with recursive self-improvement (RSI), the idea that AI systems can improve their own capabilities. But co-founder Michael Elabd argues that keeping humans involved in the training process makes Trajectory’s approach safer, since autonomous self-improvement could lead to unintended or even dangerous outcomes.
Trajectory isn’t the only startup trying to automate post-training. It considers Thinking Machines Lab, founded by former OpenAI CTO Mira Murati and the company behind the fine-tuning platform Tinker, a direct competitor, along with Applied Compute, which was valued at $3.25 billion last month, and Prime Intellect, which helps companies build and train their own AI agents.
Malde believes the startup’s biggest advantage is its team. He was among the handful of researchers Google DeepMind acqui-hired from Windsurf. Elabd also worked at DeepMind, while Trajectory’s third co-founder, Arjun Karanam, previously worked on generative foundation models at Apple. The startup has also recruited researchers and product designers from OpenAI, Stripe, and Netflix.
Why does design matter so much to a company focused on AI training? Karanam says the startup has developed an interface that makes post-training more accessible to its nontechnical customers.
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