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Learning from real-world environments follows a precise log-sigmoid scaling law, with agent performance and learning speed improving dramatically over time.
Cultural competence in language models is more about pre-training exposure than multilingual fluency, revealing a critical gap in AI's understanding of cultural nuances.
Learning algorithms might excel in memorization but can falter in broader generalization, with RL outperforming SFT in transferring knowledge across contexts.
Current AI agents struggle with long-horizon professional tasks, achieving only 30% success in complex GUI workflows, revealing critical gaps in their capabilities.
