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Switch-Reasoner reveals that adaptive reasoning selection can enhance MLLM performance by reducing unnecessary cognitive load while maintaining accuracy.
Code-executing agents can autonomously generate new, solvable math problems that are harder than existing ones, offering a scalable solution to the bottleneck of high-quality training data for advanced LLMs.
