This is a very impressive result. OpenAI was able to achieve 72% with o3, but that's at a very high compute cost at inference-time.
I'd be interested for Aide to release more metrics on token counts, total expenditure, etc. to better understand exactly how much test-time compute is involved here. They allude to it being a lot, but it would be nice to compare with OpenAI's o3.
ngl the total expenditure was around $10k, in terms of test-time compute we ran upto 20X agents on the same problem to first understand if the bitter lesson paradigm of "scale is the answer" really holds true.
The final submission which we did ran 5X agents and the decider was based on mean average score of the rewards, per problem the cost was around $20
We are going to push this scaling paradigm a bit more, my honest gut feeling is that swe-bench as a benchmark is prime for saturation real soon
1. These problem statements are in the training data for the LLMs
2. Brute-forcing the answer the way we are doing works and we just proved it, so someone is going to take a better stab at it real soon
I'd be interested for Aide to release more metrics on token counts, total expenditure, etc. to better understand exactly how much test-time compute is involved here. They allude to it being a lot, but it would be nice to compare with OpenAI's o3.