Jesse Hu discusses benchmark contamination checks performed by OpenAI researchers on the MLE-bench evaluation suite.
Insight
Jesse Hu: SWE-bench gains over baseline GPT-4 come entirely from agent scaffolding
“The diff between that and something like Devin is all in like, sort of like the agent scaffold or the agent code, right? So that's, what's really exciting about this stuff. It shows off what you can do just from prompting and just from adding tools.”
Assertion Supported
SWE-Bench public test splits enable trivial cheating via runtime PR retrieval
“The entire test split here is public. So you can do things like just overfit to the patches in the test set. You can do things like, let me add at runtime, pull the PR and just get the answer and just use it.”
Insight
Jesse Hu: SWE-Bench trajectory requirements deter commercial agents from submitting
“They actually require you to submit these things called trajectories that are proving what you did and that you're not cheating. And a lot of this ends in controversy. A lot of this ends in people say, well, we don't want to submit at all because if we review …”
Opinion
Hu: SWE-bench repos do not represent typical app development work
“And I'll say the critique here is that these are, like, really robust you know, Repos that are, have a lot of history, have a lot of people, but they don't really represent like what it is to do day-to-day work for us that are building like apps and client thi…”
Opinion
Hu: Long-context accuracy degrades; RAG remains necessary for entire large codebases
“My guess would be that, like, long context works, but it's sort of a lie as far as your accuracy, and that rag matters no matter what, because even in the longest context windows, you can't fit the whole code base.”
Assertion Supported
Hu: OpenAI o1-preview surpasses human Kaggle Grandmasters with seven gold medals
“Since a grandmaster requires five gold medals and oh, and preview gets an average of eight or sorry, seven gold medals. They're out competing even capital grandmasters.”