Data-Efficient RLVR via Off-Policy Influence Guidance

arXiv — cs.LGFriday, October 31, 2025 at 4:00:00 AM
A new approach to data selection in Reinforcement Learning with Verifiable Rewards (RLVR) has been proposed, which uses influence functions to better estimate how each data point contributes to learning. This method aims to improve the reasoning capabilities of large language models, moving beyond current heuristic-based techniques that lack theoretical backing. This advancement is significant as it could lead to more reliable and efficient learning processes in AI, enhancing the overall performance of language models.
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