RLAIF-V: 10K-100K Multimodal Preference Alignment Records

RLAIF-V provides between 10,000 and 100,000 multimodal preference-alignment records developed by OpenBMB to improve Multimodal Large Language Model (MLLM) trustworthiness. The data utilizes AI-generated feedback to refine model responses, serving as a core training component for the MiniCPM-V 4.5 model released in 2024.

Use Cases

• Preference-alignment training for MLLMs using the feedback and preference labels
• Hallucination reduction in visual question answering tasks by training on trustworthiness-oriented feedback
• Developing reward models for multimodal RLHF/RLAIF pipelines using the image-text-feedback triplets

Strengths

• Scale of 10,000 to 100,000 multimodal records
• Methodology peer-reviewed and accepted by CVPR 2025
• Proven utility in training GPT-4o level models like MiniCPM-V 4.5

Limitations

• Restricted to non-commercial use via CC BY-NC 4.0 license
• Potential for synthetic biases inherent in AI-generated feedback (RLAIF) compared to human annotation

Provenance

Source

OpenBMB and the RLAIF-V research team

Collection Method

Reinforcement Learning from AI Feedback (RLAIF)

Freshness

Last updated October 2025; reflects state-of-the-art RLAIF techniques as of CVPR 2025.