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Reinforcement Learning with Verifiable Rewards (RLVR) performs well on problems with clear rewards, such as mathematics and coding, but whether it also works where the reward is less clear remains open. The reason-over-search recipe applies RLVR to open-domain question answering, where retrieval grounds the answer and...
Evaluation metrics for safe RL are introduced that address each of these concerns and in addition allow for aggregation across tasks and safety bounds and an open-source evaluation suite to support the reliable characterization of safety in future safe RL research is provided.
This work splits each flagged answer into individual factual claims, checks each against the retrieved source, and compares leaving the answer untouched with three repair strategies of increasing richness: deleting an unsupported claim, replacing it with source text, and rewriting it.
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