A Curriculum Learning Approach to Reinforcement Learning: Leveraging RAG for Multimodal Question Answering

• URL: http://arxiv.org/abs/2508.10337v1
• Date: Thu, 14 Aug 2025 04:37:56 GMT
• Title: A Curriculum Learning Approach to Reinforcement Learning: Leveraging RAG for Multimodal Question Answering
• Abstract summary: This paper describes the solutions of the Dianping-Trust-Safety team for the META CRAG-MM challenge.<n>The challenge requires building a comprehensive retrieval-augmented generation system capable for multi-modal multi-turn question answering.
• Score: 9.67216102080208
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper describes the solutions of the Dianping-Trust-Safety team for the META CRAG-MM challenge. The challenge requires building a comprehensive retrieval-augmented generation system capable for multi-modal multi-turn question answering. The competition consists of three tasks: (1) answering questions using structured data retrieved from an image-based mock knowledge graph, (2) synthesizing information from both knowledge graphs and web search results, and (3) handling multi-turn conversations that require context understanding and information aggregation from multiple sources. For Task 1, our solution is based on the vision large language model, enhanced by supervised fine-tuning with knowledge distilled from GPT-4.1. We further applied curriculum learning strategies to guide reinforcement learning, resulting in improved answer accuracy and reduced hallucination. For Task 2 and Task 3, we additionally leveraged web search APIs to incorporate external knowledge, enabling the system to better handle complex queries and multi-turn conversations. Our approach achieved 1st place in Task 1 with a significant lead of 52.38\%, and 3rd place in Task 3, demonstrating the effectiveness of the integration of curriculum learning with reinforcement learning in our training pipeline.

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