AIBullisharXiv – CS AI · Mar 117/10
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Reinforcing Numerical Reasoning in LLMs for Tabular Prediction via Structural Priors
Researchers propose PRPO (Permutation Relative Policy Optimization), a reinforcement learning framework that enhances large language models' numerical reasoning capabilities for tabular data prediction. The method achieves performance comparable to supervised baselines while excelling in zero-shot scenarios, with an 8B parameter model outperforming much larger models by up to 53.17%.