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ARXIV:2603.16600 · AI / MACHINE LEARNING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.16600AI / MACHINE LEARNINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Proxy-GRM enhances reward modeling by generating transferable rubrics verified through proxy agents, reducing training data needs and maintaining performance.
Opportunity summary
Pain Proxy-GRM enhances reward modeling by generating transferable rubrics verified through proxy agents, reducing training data needs and maintaining performance.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Proxy-GRM enhances reward modeling by generating transferable rubrics verified through proxy agents, reducing training data needs and maintaining performance. However, the intermediate rubric is rarely optimized directly.
Generative reward models (GRMs) for vision-language models (VLMs) often evaluate outputs via a three-stage pipeline: rubric generation, criterion-based scoring, and a final verdict. However, the intermediate rubric is rarely optimized directly.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. With ~50k data samples, Proxy-GRM reaches state-of-the-art results on the VL-Reward Bench, Multimodal Reward Bench, and MM-RLHF-Reward Bench, outperforming the methods trained on four…
AI / Machine Learning moved forward this cycle; last verified April 2026. Public score 7.0/10.
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Proxy-GRM enhances reward modeling by generating transferable rubrics verified through proxy agents, reducing training data needs and maintaining performance.
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10.48550/arXiv.2603.16600Proxy-GRM enhances reward modeling by generating transferable rubrics verified through proxy agents, reducing training data needs and maintaining performance.
Abstract
Generative reward models (GRMs) for vision-language models (VLMs) often evaluate outputs via a three-stage pipeline: rubric generation, criterion-based scoring, and a final verdict. However, the intermediate rubric is rarely optimized directly. Prior work typically either treats rubrics as incidental or relies on expensive LLM-as-judge checks that provide no differentiable signal and limited training-time guidance. We propose Proxy-GRM, which introduces proxy-guided rubric verification into Reinforcement Learning (RL) to explicitly enhance rubric quality. Concretely, we train lightweight proxy agents (Proxy-SFT and Proxy-RL) that take a candidate rubric together with the original query and preference pair, and then predict the preference ordering using only the rubric as evidence. The proxy's prediction accuracy serves as a rubric-quality reward, incentivizing the model to produce rubrics that are internally consistent and transferable. With ~50k data samples, Proxy-GRM reaches state-of-the-art results on the VL-Reward Bench, Multimodal Reward Bench, and MM-RLHF-Reward Bench, outperforming the methods trained on four times the data. Ablations show Proxy-SFT is a stronger verifier than Proxy-RL, and implicit reward aggregation performs best. Crucially, the learned rubrics transfer to unseen evaluators, improving reward accuracy at test time without additional training. Our code is available at https://github.com/Qwen-Applications/Proxy-GRM.
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unverified0 refs; 0 sources; 17% coverage.
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Dimensions overall score 7.0
PROBLEM
Proxy-GRM enhances reward modeling by generating transferable rubrics verified through proxy agents, reducing training data needs and maintaining performance. However, the intermediate rubric is rarely optimized directly.
METHOD
Generative reward models (GRMs) for vision-language models (VLMs) often evaluate outputs via a three-stage pipeline: rubric generation, criterion-based scoring, and a final verdict. However, the intermediate rubric is rarely optimized directly.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. With ~50k data samples, Proxy-GRM reaches state-of-the-art results on the VL-Reward Bench, Multimodal Reward Bench, and MM-RLHF-Reward Bench, outperforming the methods trained on four times the data.
WHY NOW
AI / Machine Learning moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Proxy-GRM enhances reward modeling by generating transferable rubrics verified through proxy agents, reducing training data needs and maintaining performance. However, the intermediate rubric is rarely optimized directly.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Generative reward models (GRMs) for vision-language models (VLMs) often evaluate outputs via a three-stage pipeline: rubric generation, criterion-based scoring, and a final verdict. However, the intermediate rubric is rarely optimized directly.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. With ~50k data samples, Proxy-GRM reaches state-of-the-art results on the VL-Reward Bench, Multimodal Reward Bench, and MM-RLHF-Reward Bench, outperforming the methods trained on four times the data.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
AI / Machine Learning moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Proxy-GRM enhances reward modeling by generating transferable rubrics verified through proxy agents, reducing training data needs and maintaining performance.
Segment
AI / Machine Learning
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Commercial read
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