Pinned post
Locus sets SOTA on automated post-training capabilities and produces a model that's in production facing millions of users, completely end-to-end!
Here's a glimpse to the endless possibilities we're materializing with our research on automated scientific discovery:
Here's a glimpse to the endless possibilities we're materializing with our research on automated scientific discovery:
The models are improving the models.
Locus, our automated AI research system, is SOTA on PostTrainBench and post-trains Qwen3 base models that surpass the human post-trained Qwen3 model.
Today, LLMs post-trained end-to-end by Locus are in production to millions. 🧵👇
PostTrainBench evaluates agents' ability to post-train models on various domains given 10 H100 hours.
We extend PostTrainBench via PostTrainBench+, which has a greatly expanded compute budget that provides clearer signal on automated post-training capabilities. We find that thousands of H100 hours help distinguish methods' performance post-training Qwen3 1.7B-Base models, and that Locus scales best. In this setting, modes trained by Locus collectively surpass the perforamce of the offical human post-trained Qwen3 1.7B model.
In a test of generalization, we ran Locus on all live Kaggle competitions with prize money and public leaderboards. After 16 days, Locus achieved the 4th highest average rank among all participants.View quoted post on X
