SynthPert fine-tunes LLMs using synthetic reasoning traces to reach state-of-the-art on the PerturbQA benchmark for cellular perturbation prediction, surpassing the generating frontier model while generalizing to unseen cell types with only 2% of filtered data.
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Derives a novel two-point deterministic equivalence for random matrix resolvents to obtain unified asymptotics for SGD-trained linear regression, kernel regression, and random feature models.
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SynthPert: Enhancing LLM Biological Reasoning via Synthetic Reasoning Traces for Cellular Perturbation Prediction
SynthPert fine-tunes LLMs using synthetic reasoning traces to reach state-of-the-art on the PerturbQA benchmark for cellular perturbation prediction, surpassing the generating frontier model while generalizing to unseen cell types with only 2% of filtered data.
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Two-Point Deterministic Equivalence for Stochastic Gradient Dynamics in Linear Models
Derives a novel two-point deterministic equivalence for random matrix resolvents to obtain unified asymptotics for SGD-trained linear regression, kernel regression, and random feature models.