3 ms·
This benchmark matches my experience with GPT (I occasionally go back to Claude when I run into limits and frequently run into forgotten requirements and reward
by vanuatu 5mo ago
This benchmark matches my experience with GPT (I occasionally go back to Claude when I run into limits and frequently run into forgotten requirements and reward hacking)
I do have two questions / critiques:
- The verifier doesn't seem to check for code quality / maintainability, which I would posit is one of the major qualms with SOTA coding models i.e. they lack code 'taste'. Ofc this is a difficult problem to solve at scale, but wanted to point that out nonetheless
- This almost feels written like a critique on SWE Bench Pro. Hopefully they fix the issues with that benchmark!
- vanuatu 5mo agoOut of curiosity, I examined the worst task: https://deepswe.datacurve.ai/data/trials/quill-shared-toolbar-focus__vtVq9st https://deepswe.datacurve.ai/data/trials/quill-shared-toolba... It seems like GPT here is failing due to an environment issue of connecting to chromium, even though its local unit tests passed. All the models failed 4/4 and checking Opus it ran into the same problem I checked some other tasks and they seemed legit, although in general the prompts seem somewhat contrived vs. what a typical user would ask their coding agent (such is the difficulty of benchmark construction)
- flakiness 5mo ago> Prompts are shorter than SWE-Bench Pro's but still longer than how developers actually message agents. Behavioral verification needs some minimum specificity to know what surface to test against, which puts a floor on how terse a prompt can be before the test becomes ambiguous. It'll be tricky to automate the verification with a vague prompt. In other words, the SWE's job these days is to be a intelligent verifier.