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Weijie Qiu
Alibaba
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Dai Guan
Alibaba
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Junxin Wang
Institute of Automation, Chinese Academy of Sciences
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Zhihang Li
Alibaba
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Founder's Pitch
"Proxy-GRM enhances reward modeling by generating transferable rubrics verified through proxy agents, reducing training data needs and maintaining performance."
Commercial Viability Breakdown
0-10 scaleHigh Potential
2/4 signals
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4/4 signals
Series A Potential
2/4 signals
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arXiv Paper
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Why It Matters
This research introduces a novel framework for generating and verifying rubrics that can enhance the evaluation process in vision-language models (VLMs), improving the transferability and robustness of generated rubrics without requiring an external, computationally expensive judgment model.
Product Angle
Productize as a tool for educational assessments or professional review processes where rubric-based evaluation is common, incorporating proxy-based verification for consistency and accuracy.
Disruption
It could replace traditional expert grading systems and expensive external evaluation modules by providing a cost-effective, scalable solution for standardized assessments.
Product Opportunity
The educational sector, professional assessments, and HR departments could benefit from a tool that standardizes and verifies rubric-based evaluations, offering cost savings and reliability in grading processes.
Use Case Idea
Develop a SaaS platform for educational institutions that evaluates student assignments using rubric-based assessments verified by proxy models, ensuring consistency and reducing grading errors.
Science
The paper proposes a proxy-guided approach where proxy agents evaluate the transferability of rubrics generated by a generative reward model (GRM) for VLMs. This involves using a proxy model to verify rubric quality by ensuring it can guide an independent model to make correct judgments without re-training.
Method & Eval
The framework is evaluated on VL-RewardBench, Multimodal Reward Bench, and MM-RLHF-Reward Bench, where it achieves state-of-the-art performance using significantly less training data than competitors.
Caveats
The approach may falter if proxies themselves are biased or lack generalizability. Also, domain-specific rubrics might need custom proxies, complicating deployment.
Author Intelligence
Weijie Qiu
Alibaba
Dai Guan
Alibaba
Junxin Wang
Institute of Automation, Chinese Academy of Sciences
Zhihang Li
Alibaba
Yongbo Gai
Alibaba
Mengyu Zhou
Alibaba
Erchao Zhao
Alibaba
Xiaoxi Jiang
Alibaba
Guanjun Jiang
Alibaba