Aman Sanger

It's an open research question, one that we're quite interested in. And then there's also uncertainty of like, do you want the model to be the thing that end to end is doing everything, i.e. it's doing the retrieval and its internals and then kind of answering the question, creating the code?

It's an open research question, one that we're quite interested in. And then there's also uncertainty of like, do you want the model to be the thing that end to end is doing everything, i.e. it's doing the retrieval and its internals and then kind of answering the question, creating the code?

It's an open research question, one that we're quite interested in. And then there's also uncertainty of like, do you want the model to be the thing that end to end is doing everything, i.e. it's doing the retrieval and its internals and then kind of answering the question, creating the code?

Or do you want to separate the retrieval from the frontier model where maybe, you know, you'll get some really capable models that are much better than like the best open source ones in a handful of months? Yeah. And then you'll want to separately train a really good open source model to be the retriever, to be the thing that feeds in the context to these larger models.

Or do you want to separate the retrieval from the frontier model where maybe, you know, you'll get some really capable models that are much better than like the best open source ones in a handful of months? Yeah. And then you'll want to separately train a really good open source model to be the retriever, to be the thing that feeds in the context to these larger models.

Or do you want to separate the retrieval from the frontier model where maybe, you know, you'll get some really capable models that are much better than like the best open source ones in a handful of months? Yeah. And then you'll want to separately train a really good open source model to be the retriever, to be the thing that feeds in the context to these larger models.

Is this... Yeah, I mean, there are many possible ways you could try doing it. There's certainly no shortage of ideas. It's just a question of going in and trying all of them and being empirical about which one works best. One very naive thing is to try to replicate what's done with VS Code and these frontier models.

Is this... Yeah, I mean, there are many possible ways you could try doing it. There's certainly no shortage of ideas. It's just a question of going in and trying all of them and being empirical about which one works best. One very naive thing is to try to replicate what's done with VS Code and these frontier models.

Is this... Yeah, I mean, there are many possible ways you could try doing it. There's certainly no shortage of ideas. It's just a question of going in and trying all of them and being empirical about which one works best. One very naive thing is to try to replicate what's done with VS Code and these frontier models.

So let's continue pre-training, some kind of continued pre-training that includes general code data, but also throws in a lot of the data of some particular repository that you care about.

So let's continue pre-training, some kind of continued pre-training that includes general code data, but also throws in a lot of the data of some particular repository that you care about.

So let's continue pre-training, some kind of continued pre-training that includes general code data, but also throws in a lot of the data of some particular repository that you care about.

And then in post-training, meaning in, let's just start with instruction fine-tuning, you have like a normal instruction fine-tuning data set about code, but you throw in a lot of questions about code in that repository. So you could either get ground truth ones, which might be difficult, or you could do what you kind of hinted at or suggested using synthetic data, i.e.,

And then in post-training, meaning in, let's just start with instruction fine-tuning, you have like a normal instruction fine-tuning data set about code, but you throw in a lot of questions about code in that repository. So you could either get ground truth ones, which might be difficult, or you could do what you kind of hinted at or suggested using synthetic data, i.e.,

And then in post-training, meaning in, let's just start with instruction fine-tuning, you have like a normal instruction fine-tuning data set about code, but you throw in a lot of questions about code in that repository. So you could either get ground truth ones, which might be difficult, or you could do what you kind of hinted at or suggested using synthetic data, i.e.,

kind of having the model ask questions about various pieces of the code. So you kind of take the pieces of the code, then prompt the model or have a model propose a question for that piece of code, and then add those as instruction finds new data points. And then in theory, this might unlock the model's ability to answer questions about that code base.

kind of having the model ask questions about various pieces of the code. So you kind of take the pieces of the code, then prompt the model or have a model propose a question for that piece of code, and then add those as instruction finds new data points. And then in theory, this might unlock the model's ability to answer questions about that code base.

kind of having the model ask questions about various pieces of the code. So you kind of take the pieces of the code, then prompt the model or have a model propose a question for that piece of code, and then add those as instruction finds new data points. And then in theory, this might unlock the model's ability to answer questions about that code base.

I think test time compute is really, really interesting. So there's been the pre-training regime, which will kind of, as you scale up the amount of data and the size of your model, get you better and better performance, both on loss and then on downstream benchmarks and just general performance when we use it for coding or other tasks.

I think test time compute is really, really interesting. So there's been the pre-training regime, which will kind of, as you scale up the amount of data and the size of your model, get you better and better performance, both on loss and then on downstream benchmarks and just general performance when we use it for coding or other tasks.