Michael Elabd

Ronak Malde Verified account @rronak_ · Nov 2

Imagine if everyone had the tools to shape intelligence itself. That day is today.

Today we launch the Trajectory platform - the easiest and most intuitive tool to post-train models of your own. The foundation for continual learning.

With just a few agent prompts, you get access to a full creative suite to train, eval, and deploy trillion parameter models.

On top of that, we believe in a future where you don’t just shape your model once, it also keeps learning on its own from the world around it.

We’re releasing two research previews.

1. Continual grading scales your taste, so your model learns what good looks like from far more than you could ever grade by hand.
2. GS1, a new algorithm that lets models learn directly from what they do in production.

We’ve now raised 60M, with a new round led by @Sequoia at a 300M valuation to democratize the most powerful technology of today - training models - so that the lawyers, doctors, and engineers of the world can own their own intelligence.

You know what good looks like, now go build it. Own your own value function with Trajectory.

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Trajectory Verified account @trajectorylabs · Oct 2

Everyone talks about the algorithms that train frontier models. What fewer people talk about is all the work it takes to make a model trainable in the first place.

Here's a look inside how we did it for GLM-5.3 and GLM-5.3 Flash

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Trajectory Verified account @trajectorylabs · Sep 24

There’s a good chance your open source model is costing more than the frontier.

Cheap tokens ≠ cheap tasks.

Here, we introduce Intelligence Density, and Density Aware Training, our post-training technique to achieve less wasted compute, better learning, all with no knobs to tune.

Enabled by default in every Trajectory model.

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Trajectory Verified account @trajectorylabs · Sep 16

At Trajectory, we're constantly implementing and building upon the latest research ideas on the path to continual learning.

We wish we had the time to share all of them, but here's a quick glimpse on our explorations with PiSSA, and choosing the right trainable geometries for agentic RL.

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