RAG-RL: Advancing Retrieval-Augmented Generation via RL and Curriculum Learning

• URL: http://arxiv.org/abs/2503.12759v1
• Date: Mon, 17 Mar 2025 02:53:42 GMT
• Title: RAG-RL: Advancing Retrieval-Augmented Generation via RL and Curriculum Learning
• Authors: Jerry Huang, Siddarth Madala, Risham Sidhu, Cheng Niu, Julia Hockenmaier, Tong Zhang,
• Abstract summary: We introduce RAG-RL, the first reasoning language model (RLM) specifically trained for retrieval-augmented generation (RAG) settings.<n>RAG-RL demonstrates that stronger answer generation models can identify relevant contexts within larger sets of retrieved information.<n>We show that curriculum design in the reinforcement learning (RL) post-training process is a powerful approach to enhancing model performance.
• Score: 11.872929831119661
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent research highlights the challenges retrieval models face in retrieving useful contexts and the limitations of generation models in effectively utilizing those contexts in retrieval-augmented generation (RAG) settings. To address these challenges, we introduce RAG-RL, the first reasoning language model (RLM) specifically trained for RAG. RAG-RL demonstrates that stronger answer generation models can identify relevant contexts within larger sets of retrieved information -- thereby alleviating the burden on retrievers -- while also being able to utilize those contexts more effectively. Moreover, we show that curriculum design in the reinforcement learning (RL) post-training process is a powerful approach to enhancing model performance. We benchmark our method on two open-domain question-answering datasets and achieve state-of-the-art results, surpassing previous SOTA generative reader models. In addition, we offers empirical insights into various curriculum learning strategies, providing a deeper understanding of their impact on model performance.

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