over one million Trainium2 chips to train and serve Claude

The minimum training cutoffs are: ECI (June 2024), METR Time Horizon (January 2024), Combined Math (September 2024), and WeirdML V2 (January 2025).

Academic publishers, documentary archives, game studios, and companies sitting on years of enterprise data have all been courted for the seeds of intelligence needed to train the next generation of models.

As slop takes over the Internet, labs may struggle to obtain high-quality corpuses for training models.

Support teams are high volume and high turnover, and thus need to train new reps in a fast and standardized way. To do so, they have clearly articulated standard operating procedures (SOPs) that guide the work of each rep. These SOPs create clear rules and guidelines that AI agents can model themselves off of.

A three-stage progressive training strategy -- large-scale pre-training, hard sample fine-tuning, and GRPO alignment -- sequentially exploits these data at different quality tiers.

SOTA models of different architectures and parameter scales exhibit highly consistent failure patterns on the same set of hard samples, suggesting that the performance bottleneck stems from shared deficiencies in training data rather than architecture itself.

If we knew that every image uploaded was a beautiful model shot, segmentation would be far easier, but because of the nature of user-uploaded content, we need the best possible segmentation.

the model collapse paper now suggests that the training data created by well-managed communities could be the new currency of collective intelligence.

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