MMRAG-RFT: Two-stage Reinforcement Fine-tuning for Explainable Multi-modal Retrieval-augmented Generation
Published:Dec 19, 2025 03:19
•1 min read
•ArXiv
Analysis
The article introduces a novel approach, MMRAG-RFT, for improving explainability in multi-modal retrieval-augmented generation. The two-stage reinforcement fine-tuning strategy likely aims to optimize the model's ability to generate coherent and well-supported outputs by leveraging both retrieval and generation components. The focus on explainability suggests an attempt to address the 'black box' nature of many AI models, making the reasoning process more transparent.
Key Takeaways
- •MMRAG-RFT is a new approach for explainable multi-modal retrieval-augmented generation.
- •It utilizes a two-stage reinforcement fine-tuning strategy.
- •The goal is to improve the model's ability to generate coherent and well-supported outputs.
- •The focus on explainability aims to make the model's reasoning process more transparent.
Reference
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