AI RESEARCH
Rethinking Multiple-Choice Questions for RLVR: Unlocking Potential via Distractor Design
arXiv CS.CL
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ArXi:2603.12826v1 Announce Type: new Reinforcement Learning with Verifiable Rewards (RLVR) significantly enhances the reasoning capabilities of Large Language Models. When applied to RLVR, Multiple-Choice Questions (MCQs) offer a scalable source of verifiable data but risk inducing reward hacking, where models shortcut reasoning via random guessing or simple elimination. Current approaches often mitigate this by converting MCQs to open-ended formats, thereby discarding the contrastive signal provided by expert-designed distractors.