IRDS selects RLVR data via verifier-coupled SAE cluster coverage using greedy log-determinant maximization, reporting accuracy gains over baselines on math benchmarks.
arXiv preprint arXiv:2503.15573 , year=
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NeuFS selects active few-shot samples for LLMs by representing samples via neuron activation patterns and applying a dual-criteria strategy of diversity and neuron consensus to identify informative examples.
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IRDS: Interpretable RLVR Data Selection via Verifier-Coupled Sparse Autoencoder Coverage
IRDS selects RLVR data via verifier-coupled SAE cluster coverage using greedy log-determinant maximization, reporting accuracy gains over baselines on math benchmarks.
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Neuron-Aware Active Few-Shot Learning for LLMs
NeuFS selects active few-shot samples for LLMs by representing samples via neuron activation patterns and applying a dual-criteria strategy of diversity and neuron consensus to identify informative examples.