Viewing profile — 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 t…

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 t…

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, f…

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 t…

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.

Thanks! I do like the labs blog posts as well though, OpenAI and Anthropic have some classics.

Our primary focus is on RL post-training. We think that is the best way to get the model to be a strong interactive agent.

We did a lot of internal testing and thought this model was already quite useful for release.

Cheetah was an earlier (and dumber) version of this model that we used to test production speed. They are both developed in-house. If you liked Cheetah, give this model a try.

We like the name Composer and were sad to see it go. Excited to bring it back. (Agree Cheetah is a cool name too.)

There is a footnote that should help with the models. Training is a harder thing to report on, but roughly our finding here is that RL scales.

We don't wear shoes [1]. [1] https://www.businessinsider.com/no-shoes-policy-in-office-cu...

Agree that Sonnet 4.5 is an excellent model. Would be curious to hear your experience using Composer though, it's quite good.

We also are big Tab users here at Cursor. In the blog we talk about the motivation for this project came from thinking about a Tab-like agent.

Hi everyone, I am an ML researcher at Cursor, and worked on this project. Would love to hear any feedback you may have on the model, and can answer question about the blog post.