Summary of Re-bench: Evaluating Frontier Ai R&d Capabilities Of Language Model Agents Against Human Experts, by Hjalmar Wijk et al.
RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts
by Hjalmar Wijk, Tao Lin, Joel Becker, Sami Jawhar, Neev Parikh, Thomas Broadley, Lawrence Chan, Michael Chen, Josh Clymer, Jai Dhyani, Elena Ericheva, Katharyn Garcia, Brian Goodrich, Nikola Jurkovic, Megan Kinniment, Aron Lajko, Seraphina Nix, Lucas Sato, William Saunders, Maksym Taran, Ben West, Elizabeth Barnes
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces RE-Bench (Research Engineering Benchmark), a novel framework for evaluating AI research and development capabilities by simulating open-ended machine learning tasks. The benchmark consists of seven challenging environments, with data from 71 attempts by 61 human experts. The results show that humans make progress in the environments given eight hours, but are outperformed by AI agents when given a shorter time budget. However, humans display better returns to increasing time budgets and achieve higher scores when given more total hours. The paper also highlights the capabilities of modern AI agents, which can generate and test solutions over ten times faster than humans at much lower cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers created a benchmark called RE-Bench to help evaluate how well AI systems can do research and development tasks. They made seven challenging environments that require human experts to work on machine learning problems. The results showed that the experts did well in these environments, but AI systems were even better when they had less time to complete the tasks. However, the humans were better at using more time to get better results. This study also found that AI systems are very good at coming up with and testing solutions quickly and cheaply. |
Keywords
* Artificial intelligence * Machine learning
