srush

Awesome to hear, I will share with the team.

Roughly, we had Cursor software engineers record real questions they were asking models, and then had them record the PR that they made that contained the result. We then cleaned these up. That is the benchmark.

A lot of people use it! It scores very well on our benchmarks, significantly better than Composer-1.

We use Ray data for our map-style processing jobs. For example one tool have runs over all the rollouts from the RL system and collects qualitative statistics to understand which type of agent trajectories are being reward, and what types of searches and terminal commands are being made.

Oh good question. Actually speaking at the Ray Summit next week in SF so we will talk more about it. We used Ray throughout the pipeline for running evals, for the RL controller, for data collation, and for visualizations. One tool we found helpful was Ray Data which let us easily scale over data and run logs.

We train with a single agent. is that the question?

neat!

Our view is that there is a now a minimal amount of intelligence that is necessary to be productive, and that if you can pair that with speed that is awesome.

Let us know how you like it.

Straight up untrue.

There are lots of good models we like here. But we agree that getting the right point on the smart+fast graph can make agentic coding feel really good.

(Cursor researcher)

Thanks! Yeah, been working here for 9 months now. Fascinated byt agentic coding both as a researcher and user.

Totally agree that "smart model" is the table stakes for usefulness these days.

We will do our best. Luckily I don't think there are major telecom companies called Composer-2.

Unfortunately not, as we used our own internal code for the benchmark. We would also like to see more benchmarks that reflect the day-to-day agentic coding use.

The blog talks about the training process. Specifically we trained with RL post-training on coding examples.