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Local-Preserving Supervised Fine-Tuning can enhance model performance without sacrificing the rich diversity of pretrained knowledge, achieving superior results in both accuracy and diversity metrics.
Learning from real-world environments follows a precise log-sigmoid scaling law, with agent performance and learning speed improving dramatically over time.
