(from Cursor's blog)

> Training included trillions of tokens of Cursor data which capture a wide-range of user interactions with codebases and software tools. This dataset lets the model learn both from existing software as well as developer-agent interactions, capturing how developers work and how agents interact with their environments.

This is what the big money was for. Cursor is the first big player that had real-world data from real-world projects, before cc / codex were a thing.

> We used reinforcement learning on difficult problems in realistic environments spanning both software engineering and broader knowledge work. These environments teach the model to investigate problems, use tools, recover from mistakes, and verify results.

> Many of these problems had to be designed to be difficult enough that even frontier models fail at them. As models improve, existing tasks stop teaching them anything new, and problems that once required extensive reasoning become routine.

> We developed a distributed agent system to construct these environments at scale. Engineers specify a problem and how a solution is verified, and large groups of agents construct, test, and refine each environment.

This is where scale comes in. You use the previous gen model to prepare datasets for the next model iteration. The better the models, the better the data, the better the next models. (they also have a comparison with their composer2.5 training run, for people still thinking chinese models are "close to SotA"...)

Reports of xAIs demise (after giving a lot of compute to Anthropic) were slightly exaggerated, it seems.

> Grok 4.5 was trained across tens of thousands of NVIDIA GB300 GPUs

well the big money was also in spacex stock, fresh post IPO, so overall a very smart move it seems