SVR learns a bank of contrastive rubrics from preference data via max-margin boundaries and prompt-conditioned selection, narrowing the gap to human rubrics on RubricBench from 24.1 to 0.3 points.
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arXiv preprint arXiv:2602.01511 , year=
14 Pith papers cite this work. Polarity classification is still indexing.
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2026 14representative citing papers
ARBOR introduces a reusable rubric buffer that consolidates contrastive trajectory drafts into cross-query rubrics for online process rewards, outperforming GRPO and DAPO on multi-hop QA benchmarks.
A GRPO-trained rubric generator that injects step-level criteria into ReAct agents improves deep-research benchmark scores over several test-time compute baselines.
AutoRubric-T2I learns and selects explicit rubrics from preference pairs to guide VLM judges, producing high-quality interpretable rewards for T2I alignment with far less data than traditional Bradley-Terry models.
rDPO uses offline-built rubrics to generate on-policy preference data for DPO, raising benchmark scores in visual tasks over outcome-based filtering and style baselines.
ARL-RR alternates optimization over rubric meta-classes with dynamic selection to avoid fixed scalarization, outperforming baselines on HealthBench.
ARCO co-trains an LLM agent with a per-step rubric model whose natural-language criteria and scoring function evolve with the policy, and reports the best exact-match accuracy in all six benchmark/backbone settings compared.
RUBRIC-ARROW is an alternating rubric generator and judge framework that uses probability-based scoring and pairwise preferences to improve pointwise reward modeling accuracy for LLM post-training in non-verifiable domains.
GrowLoop proposes a human-seeded self-evolving framework that co-evolves rubrics and cases to evaluate conversational human-likeness with differentiated agreement rules.
CLR-voyance reformulates inpatient reasoning as POMDP with clinician-validated outcome rubrics, yielding an 8B model that outperforms larger frontier models on the authors' new benchmark.
DeltaRubric decomposes multimodal preference evaluation into self-generated planning and verification steps within a single model, producing large accuracy improvements on VL-RewardBench via multi-role reinforcement learning.
C2 synthesizes contrastive helpful/misleading rubric pairs from binary preferences to train cooperative generators and critical verifiers, yielding up to 6.5-point gains on RM-Bench and enabling smaller models to match larger rubric-augmented ones.
RLR³ extends RLVR to criterion-level rubric verification via dual execution paths, minimal exposure masking, hierarchical aggregation, and saturation mitigation, delivering 4.7-point gains over base on 15 benchmarks with Qwen3-VL-30B-A3B.
citing papers explorer
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Support Vector Rubrics: Closing the Gap Between Self-Generated and Human Rubrics
SVR learns a bank of contrastive rubrics from preference data via max-margin boundaries and prompt-conditioned selection, narrowing the gap to human rubrics on RubricBench from 24.1 to 0.3 points.
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ARBOR: Online Process Rewards via a Reusable Rubric Buffer for Search Agents
ARBOR introduces a reusable rubric buffer that consolidates contrastive trajectory drafts into cross-query rubrics for online process rewards, outperforming GRPO and DAPO on multi-hop QA benchmarks.
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Co-ReAct: Rubrics as Step-Level Collaborators for ReAct Agents
A GRPO-trained rubric generator that injects step-level criteria into ReAct agents improves deep-research benchmark scores over several test-time compute baselines.
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AutoRubric-T2I: Robust Rule-Based Reward Model for Text-to-Image Alignment
AutoRubric-T2I learns and selects explicit rubrics from preference pairs to guide VLM judges, producing high-quality interpretable rewards for T2I alignment with far less data than traditional Bradley-Terry models.
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Visual Preference Optimization with Rubric Rewards
rDPO uses offline-built rubrics to generate on-policy preference data for DPO, raising benchmark scores in visual tasks over outcome-based filtering and style baselines.
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Alternating Reinforcement Learning with Contextual Rubric Rewards: Beyond the Scalarization Strategy
ARL-RR alternates optimization over rubric meta-classes with dynamic selection to avoid fixed scalarization, outperforming baselines on HealthBench.
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ARCO: Adaptive Rubrics with Co-Evolution for Multi-Step LLM-Based Agents
ARCO co-trains an LLM agent with a per-step rubric model whose natural-language criteria and scoring function evolve with the policy, and reports the best exact-match accuracy in all six benchmark/backbone settings compared.
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RUBRIC-ARROW: Alternating Pointwise Rubric Reward Modeling for LLM Post-training in Non-verifiable Domains
RUBRIC-ARROW is an alternating rubric generator and judge framework that uses probability-based scoring and pairwise preferences to improve pointwise reward modeling accuracy for LLM post-training in non-verifiable domains.
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GrowLoop: Self-Evolving Conversation Evaluation Seeded by Human
GrowLoop proposes a human-seeded self-evolving framework that co-evolves rubrics and cases to evaluate conversational human-likeness with differentiated agreement rules.
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CLR-voyance: Reinforcing Open-Ended Reasoning for Inpatient Clinical Decision Support with Outcome-Aware Rubrics
CLR-voyance reformulates inpatient reasoning as POMDP with clinician-validated outcome rubrics, yielding an 8B model that outperforms larger frontier models on the authors' new benchmark.
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DeltaRubric: Generative Multimodal Reward Modeling via Joint Planning and Verification
DeltaRubric decomposes multimodal preference evaluation into self-generated planning and verification steps within a single model, producing large accuracy improvements on VL-RewardBench via multi-role reinforcement learning.
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C2: Scalable Rubric-Augmented Reward Modeling from Binary Preferences
C2 synthesizes contrastive helpful/misleading rubric pairs from binary preferences to train cooperative generators and critical verifiers, yielding up to 6.5-point gains on RM-Bench and enabling smaller models to match larger rubric-augmented ones.
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Reinforcement Learning with Robust Rubric Rewards
RLR³ extends RLVR to criterion-level rubric verification via dual execution paths, minimal exposure masking, hierarchical aggregation, and saturation mitigation, delivering 4.7-point gains over base on 15 benchmarks with Qwen3-VL-30B-A3B.
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