TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework

Retrieval-Augmented Generation (RAG) utilizes external knowledge to augment Large Language Models' (LLMs) reliability.

For flexibility, agentic RAG employs autonomous, multi-round retrieval and reasoning to resolve queries.

Although recent agentic RAG has improved via reinforcement learning, they often incur substantial token overhead from search and reasoning processes.

This trade-off prioritizes accuracy over efficiency.

To address this issue, this work proposes TeaRAG, a token-efficient agentic RAG framework capable of compressing both retrieval content and reasoning steps.

1) First, the retrieved content is compressed by augmenting chunk-based semantic retrieval with a graph retrieval using concise triplets.

A knowledge association graph is then built from semantic similarity and co-occurrence.

Finally, Personalized PageRank is leveraged to highlight key knowledge within this graph, reducing the number of tokens per retrieval.

2) Besides, to reduce reasoning steps, Iterative Process-aware Direct Preference Optimization (IP-DPO) is proposed.

Specifically, our reward function evaluates the knowledge sufficiency by a knowledge matching mechanism, while penalizing excessive reasoning steps.

This design can produce high-quality preference-pair datasets, supporting iterative DPO to improve reasoning conciseness.

Across six datasets, TeaRAG improves the average Exact Match by 4% and 2% while reducing output tokens by 61% and 59% on Llama3-8B-Instruct and Qwen2.5-14B-Instruct, respectively.

Code is available at this https URL.