MARAG-R1: Beyond Single Retriever via Reinforcement-Learned Multi-Tool Agentic Retrieval (2510.27569v1)

Abstract: LLMs excel at reasoning and generation but are inherently limited by static pretraining data, resulting in factual inaccuracies and weak adaptability to new information. Retrieval-Augmented Generation (RAG) addresses this issue by grounding LLMs in external knowledge; However, the effectiveness of RAG critically depends on whether the model can adequately access relevant information. Existing RAG systems rely on a single retriever with fixed top-k selection, restricting access to a narrow and static subset of the corpus. As a result, this single-retriever paradigm has become the primary bottleneck for comprehensive external information acquisition, especially in tasks requiring corpus-level reasoning. To overcome this limitation, we propose MARAG-R1, a reinforcement-learned multi-tool RAG framework that enables LLMs to dynamically coordinate multiple retrieval mechanisms for broader and more precise information access. MARAG-R1 equips the model with four retrieval tools -- semantic search, keyword search, filtering, and aggregation -- and learns both how and when to use them through a two-stage training process: supervised fine-tuning followed by reinforcement learning. This design allows the model to interleave reasoning and retrieval, progressively gathering sufficient evidence for corpus-level synthesis. Experiments on GlobalQA, HotpotQA, and 2WikiMultiHopQA demonstrate that MARAG-R1 substantially outperforms strong baselines and achieves new state-of-the-art results in corpus-level reasoning tasks.

• The paper introduces MARAG-R1, a framework that leverages multi-tool retrieval and reinforcement learning to synthesize information from extensive corpora.
• It employs a dual-stage training process with supervised fine-tuning and reinforcement learning to strategically guide tool usage.
• Evaluations on benchmarks like GlobalQA and HotpotQA demonstrate significant improvements in answer accuracy and document coverage.

Summary of "MARAG-R1: Beyond Single Retriever via Reinforcement-Learned Multi-Tool Agentic Retrieval" (2510.27569)

The paper introduces the MARAG-R1 framework, designed to enhance Retrieval-Augmented Generation (RAG) systems by moving beyond the limitations of single-retriever models. By incorporating multiple retrieval tools and employing reinforcement learning, MARAG-R1 achieves superior information retrieval and reasoning capabilities, especially in tasks requiring corpus-level synthesis. This essay provides a detailed examination of MARAG-R1, its implementation, performance, and implications for future developments in AI retrieval methods.

Framework and Novel Contributions

Limitations of Existing RAG Systems

Traditional RAG systems rely on a single retriever, often leading to information bottlenecks. They operate by selecting a fixed top-$k$ subset of documents for answer generation. This method restricts the model's adaptability and access to the complete corpus, hindering its ability to resolve tasks necessitating comprehensive reasoning.

Multi-Tool Strategy in MARAG-R1

MARAG-R1 innovatively equips Models with four retrieval tools: semantic search, keyword search, filtering, and aggregation. The framework employs a two-stage training approach:

1. Supervised Fine-Tuning (SFT): Initially provides the model with foundational knowledge on tool usage.
2. Reinforcement Learning (RL): Refines the multi-tool coordination through rewards that promote effective retrieval and decision-making.

This dual-stage training equips the model with the versatility needed to dynamically access diverse information sources, thereby enhancing its reasoning and generative accuracy.

Training Process and Implementation Details

Trajectory Collection

The framework begins by collecting expert trajectories, designing retrieval tools to support various retrieval demands, thereby structifying how each tool should be utilized for a given query.

Reinforcement Learning and Reward Design

A composite reward system is employed, comprising:

• Answer Reward ($R_A$): Evaluates the precision of the generated answers.
• Document Coverage Reward ($R_E$): Assesses completeness and precision of retrieved document sets.
• Tool Exploration Reward ($R_T$): Encourages strategic tool usage without excessive redundancy.

This reward system guides the model toward maximizing evidence coverage and reasoning completeness.

Experimental Results

Superior Performance Across Benchmarks

MARAG-R1 demonstrates state-of-the-art performance across various datasets (GlobalQA, HotpotQA, and 2WikiMultiHopQA) and tasks (TopK, Count, Sort, MinMax), exceeding existing baselines in both answer accuracy (F1) and document coverage (D-F1@20).

Figure 1: F1/D-F1@20 performance of MARAG-R1 and ReCall under different retrieval steps.

Ablation Studies

Ablation studies reveal each component's critical role, with supervised fine-tuning and reinforcement learning each significantly enhancing overall performance. Removing any retrieval tool or reward term results in measurable performance degradation, underscoring their synergistic contribution to MARAG-R1’s efficacy.

Generalization and Implications

Application to Multi-Hop QA Tasks

MARAG-R1's design enables effective generalization to multi-hop reasoning tasks, highlighting its broader applicability beyond the tested datasets. It efficiently acquires and utilizes external information, maintaining high accuracy across varied contexts.

Future Directions

By demonstrating a flexible framework for integrating dynamic retrieval strategies, MARAG-R1 sets a foundation for further enhancements in AI’s ability to process and synthesize large-scale information systems. Future work may involve optimizing retrieval paths and exploring additional tools or hybrid models to further expand the system’s reasoning capabilities.

Conclusion

MARAG-R1 represents a significant advancement in Retrieval-Augmented Generation, offering a comprehensive strategy for overcoming traditional RAG systems’ limitations. By leveraging multi-tool coordination and reinforcement learning, it not only enhances retrieval accuracy but also ensures deeper reasoning capabilities, marking a pivotal step towards more intelligent and adaptive AI systems.