OpenAI has introduced a new benchmark called MLE-bench to evaluate the machine learning engineering capabilities of AI agents. The benchmark includes 75 machine learning engineering-related competitions sourced from Kaggle. This innovative tool aims to bridge the gap between theoretical AI knowledge and practical application in real-world scenarios. MLE-bench challenges AI systems with real-world data science tasks, revealing both the progress and limitations of AI in machine learning. OpenAI's o1-preview model with AIDE scaffolding has achieved Kaggle bronze medal standards in 16.9% of the competitions, highlighting the current capabilities and areas for improvement in AI engineering. The benchmark was developed by Chan et al., and the current best approach solves approximately 17% of the tasks.
This Paper reveals LLMs lack robust mathematical reasoning, relying on pattern matching rather than genuine conceptual understanding. Now generally till now, LLMs have shown impressive performance on grade-school math tasks like GSM8K. But it's unclear if they truly have… https://t.co/e1hIgN5U90
🏷️:MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering 🔗:https://t.co/DRjhp7PHNy https://t.co/7Ixennx4LC
Large language models (~AI) are not yet capable of true reasoning. Research from Apple scientists challenges their mathematical reasoning limits by introducing a benchmark designed to test LLMs on diverse mathematical problems. Even slight changes in numerical values or problem…