AI RESEARCH
QUBRIC: Co-Designing Queries and Rubrics for RL Beyond Verifiable Rewards
arXiv CS.CL
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ArXi:2606.03968v1 Announce Type: new Rubric-based RL is a promising route for extending reinforcement learning beyond verifiable rewards, yet existing methods optimize rubrics while treating the query distribution as fixed. We identify a structural bottleneck: rubric quality is constrained by query structure. Open-ended queries yield vague rubrics; naively narrowing them