MLE-World
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- MLE-World predicts ML experiment outcomes without running programs
MLE-World, a world model fine-tuned from Qwen3.5-4B, predicts machine-learning programs’ terminal outputs—including error traces, training logs and validation scores—without executing them, alongside a confidence score. The paper reports that on 17 MLE-bench Lite tasks, its predictions matched real outputs for 77.9% of held-out programs, versus 41.1% for much larger prompted models. Five test-time-scaling policies guided by MLE-World produced up to 22.1% higher observed mean performance with less real execution than an agent without a world model.
Hugging Face · Papers · 🔥 0
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