LIMO achieves 63.3% on AIME24 and 95.6% on MATH500 via supervised fine-tuning on roughly 1% of the data used by prior models, supporting the claim that minimal strategic examples suffice when pre-training has already encoded domain knowledge.
Minimum Error Rate Training in Statistical Machine Translation
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POTracker fine-tunes an LLM with POTrackerLoss combining textual and structural similarity, achieving up to 86.47% structural accuracy on 1,000 power outage reports and outperforming baselines by up to 51%.
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LIMO: Less is More for Reasoning
LIMO achieves 63.3% on AIME24 and 95.6% on MATH500 via supervised fine-tuning on roughly 1% of the data used by prior models, supporting the claim that minimal strategic examples suffice when pre-training has already encoded domain knowledge.
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POTracker: Optimizing Large Language Models for Standard-Compliant Power Outage Report Generation
POTracker fine-tunes an LLM with POTrackerLoss combining textual and structural similarity, achieving up to 86.47% structural accuracy on 1,000 power outage reports and outperforming baselines by up to 51%.