1 link tagged with all of: reinforcement-learning + curriculum-learning + meta-learning + model-training
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This article explores a method called SOAR, where a pre-trained model generates synthetic problems to help another model learn better. It emphasizes the importance of creating effective learning tasks rather than focusing solely on problem-solving accuracy. The findings suggest that this self-improvement approach can help models overcome learning difficulties without needing more curated data.