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Chuck Tang

Verified account Trajectory

@j316chuck

scaling compute @trajectorylabs
San Francisco Joined November 2016
813 Following 484 Followers 118 Posts

Latest posts 5

Chuck Tang Verified account @j316chuck
trajectory.ai/waitlist (◕‿◕✿)
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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Chuck Tang Verified account @j316chuck
The M in GLM stands for mountain ⛰️ (joke)
Here is a blog on how we climbed it 🧗 (not joke)
Trajectory Verified account @trajectorylabs Oct 2
The first obstacle: silent numerical mismatches.

In RL, a sampler writes answers and a trainer learns from them. Both run the same model, but with different kernels and math, they can quietly drift apart. Nothing crashes, all you just get a flat reward curve hours later. Very helpful.
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Chuck Tang Verified account @j316chuck
was very cool seeing chef @yapdianang cook up this training recipe producing:

- less model yaps
+ more model intelligence

or in other words more intelligently dense, open source models :)
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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Chuck Tang Verified account @j316chuck
goal this weekend:
- eat more pizza
- train more pissa

nice work @neilkale :)
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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Chuck Tang Verified account @j316chuck
Had a great time speaking at the SkyRL event on how Trajectory built #TCLI to serve hundreds of RL training runs a day, so our users' models keep getting better.

- Shout out to our partners at @AMD and @wafer_ai for sharing the exciting disaggregated RL result on climbing AIME benchmarks by rolling out on AMD and training on NVIDIA.
- And as always good to hear more deep dives on how SkyRL works underneath the hood (multi-LoRA, harbor integration) from @erictang000 @sumanthrh and @charlie_ruan

Btw if anyone can guess what TCLI stands for I'll buy them a burrito. 🌯 Hint: there are two right answers.
NovaSky Verified account @NovaSkyAI Aug 19
It was a packed house at the SkyRL Meetup today.

Thanks to all the speakers!
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