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ARXIV:2606.03968 · RL WITH RUBRICS · SUBMITTED 03 JUN · 20:41 UTC · FRESHNESS FRESH
ARXIV:2606.03968RL WITH RUBRICSSUBMITTED 03 JUN · 20:41 UTCFRESHNESS FRESHRongzhi Zhang · Rui Feng · Zhihan Zhang · Jingfeng Yang · Qingyu Yin · Xin Liu · +5 at arXiv
QUBRIC co-designs queries and rubrics for reinforcement learning beyond verifiable rewards, achieving significant gains on reasoning tasks.
Opportunity summary
Pain QUBRIC co-designs queries and rubrics for reinforcement learning beyond verifiable rewards, achieving significant gains on reasoning tasks.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
QUBRIC co-designs queries and rubrics for reinforcement learning beyond verifiable rewards, achieving significant gains on reasoning tasks. We identify a structural bottleneck: rubric quality is constrained by query structure.
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…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. QUBRIC achieves a +5.5 point gain on ArenaHard over the SFT baseline. Code availability is flagged in the production record; the public repository link…
RL with Rubrics moved forward this cycle; last verified June 2026. Public score 7.0/10. Production flags indicate code availability.
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QUBRIC co-designs queries and rubrics for reinforcement learning beyond verifiable rewards, achieving significant gains on reasoning tasks.
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10.48550/arXiv.2606.03968QUBRIC co-designs queries and rubrics for reinforcement learning beyond verifiable rewards, achieving significant gains on reasoning tasks.
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 model can verify, so all responses fail and training receives no reward signal. We present QUBRIC, a framework that co-designs queries and rubrics. Teacher-derived key points ground the rewriting of open-ended queries into scenario-based, evaluable questions. Contrastive rubric generation then turns teacher-policy gaps into query-level criteria, and learnability filtering retains only informative query-rubric pairs for GRPO training. QUBRIC achieves a +5.5 point gain on ArenaHard over the SFT baseline. Trained only on instruction-following data, it further transfers to three held-out benchmarks spanning legal, moral, and narrative reasoning (+6.3 points on average), with improvements concentrated in reasoning-related dimensions. These results provide evidence that co-designing queries and rubrics can make rubric-based RL a practical complement to RLVR beyond strictly verifiable tasks.
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PROBLEM
QUBRIC co-designs queries and rubrics for reinforcement learning beyond verifiable rewards, achieving significant gains on reasoning tasks. We identify a structural bottleneck: rubric quality is constrained by query structure.
METHOD
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.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. QUBRIC achieves a +5.5 point gain on ArenaHard over the SFT baseline. Code availability is flagged in the production record; the public repository link still needs proof alignment.
WHY NOW
RL with Rubrics moved forward this cycle; last verified June 2026. Public score 7.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 19, "author": "Rongzhi Zhang; Rui Feng; Zhihan Zhang; Jingfeng Yang; Qingyu Yin; Xin Liu; Zixuan Zhang; Priyanka Nigam; Bing Yin; Tuo Zhao; Chao Zhang"
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QUBRIC co-designs queries and rubrics for reinforcement learning beyond verifiable rewards, achieving significant gains on reasoning tasks.
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RL with Rubrics
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