IRDS: Interpretable RLVR Data Selection via Verifier-Coupled Sparse Autoencoder Coverage 事件

IRDS: Interpretable RLVR Data Selection via Verifier-Coupled Sparse Autoencoder Coverage arXiv:2605.28247v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a key technique for en- hancing LLM reasoning, yet its data ineffi- ciency remains a major bottleneck. Existing methods address this problem only partially, each missing at least one of subset-level cov- erage, verifier signal use, or interpretability. To address this gap, we present IRDS (Inte