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Imperial College London, University of Edinburgh, Nanyang Technological University, MBZUAI
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Pythagoras-Prover achieves state-of-the-art performance in formal proving with dramatically fewer parameters, challenging the notion that bigger models always yield better results.
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.
