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China Agricultural University
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LLMs can dramatically improve MIMO controller tuning by reasoning about complex interactions, achieving optimal performance with far fewer evaluations than traditional methods.
Idiom comprehension in low-resource languages suffers significantly, with literal meanings proving far more challenging than figurative interpretations, even in context-rich conversations.
Forget hand-crafted reward functions: $\text{RLR}^3$ leverages rubrics and LLMs to provide fine-grained, multi-criteria supervision, outperforming standard RLVR in vision-language tasks.
