Dazhong Shen
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126
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ML×1AI×1
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2026
IRDS: Interpretable RLVR Data Selection via Verifier-Coupled Sparse Autoencoder Coverage
IRDS introduces a novel data selection method that uses a verifier-coupled sparse autoencoder framework to efficiently select high-quality Reinforcement Learning with Verifiable Rewards (RLVR) training instances, achieving state-of-the-art performance on multiple reasoning benchmarks.
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