QUBRIC: Co-Designing Queries and Rubrics for RL Beyond Verifiable Rewards 事件

QUBRIC: Co-Designing Queries and Rubrics for RL Beyond Verifiable Rewards arXiv:2606.03968v1 Announce Type: new Abstract: 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 introduces fabricated references that no