PiT-PO adaptively fine-tunes an LLM during symbolic regression search using physics-validity and token-level redundancy constraints, reporting state-of-the-art benchmark results and a periodic-hill turbulence closure.
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LLM-Based Scientific Equation Discovery via Physics-Informed Token-Regularized Policy Optimization
PiT-PO adaptively fine-tunes an LLM during symbolic regression search using physics-validity and token-level redundancy constraints, reporting state-of-the-art benchmark results and a periodic-hill turbulence closure.