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    <abstract>Retrieval-Augmented Generation (RAG) has become the backbone of knowledge-intensive multi-hop question answering, yet routing every sub-query through a frontier model turns every hop into a cost multiplier and makes real-world deployment prohibitively expensive. Existing remedies either fix the retrieval schedule, route once at the query level, or lack a principled stopping rule, leaving a critical gap: no framework adapts, hop by hop, to how a trajectory actually unfolds. We introduce RAG-on-a-Diet, a lightweight reinforcement-learning agent that treats each reasoning hop as an independent decision and selects the smallest model (Qwen3-4B, Qwen3-30B, or DS-R1-671B) sufficient for it, guided by entity- and confidence-aware features. Trained via behavior cloning followed by PPO under a five-component cost-aware reward (final, cumulative, step-wise, cost, balance) and coupled with an explicit two-tier termination policy (5-hop cap plus a tau=0.3 confidence gate), the agent carves a Pareto-optimal efficiency frontier. On HotpotQA it cuts Monetary Inference Cost by 60.07% against IRCoT with only a 3.7% F1 drop; it matches Adaptive-RAG’s F1 at 37.30% lower cost; and it attains up to 2.33x higher Quality-per-Monetary-Cost. Consistent gains on MuSiQue, 2WikiMultiHopQA, CRAG, and Bamboogle confirm strong out-of-distribution robustness, setting a new paradigm for fine-grained resource control in multi-hop RAG.</abstract>
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%0 Conference Proceedings
%T RAG-on-a-Diet: A Reinforcement Learning-Based Dynamic Resource Optimization Framework for RAG
%A Ding, Hongwen
%A Zhao, Yizheng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F ding-zhao-2026-rag
%X Retrieval-Augmented Generation (RAG) has become the backbone of knowledge-intensive multi-hop question answering, yet routing every sub-query through a frontier model turns every hop into a cost multiplier and makes real-world deployment prohibitively expensive. Existing remedies either fix the retrieval schedule, route once at the query level, or lack a principled stopping rule, leaving a critical gap: no framework adapts, hop by hop, to how a trajectory actually unfolds. We introduce RAG-on-a-Diet, a lightweight reinforcement-learning agent that treats each reasoning hop as an independent decision and selects the smallest model (Qwen3-4B, Qwen3-30B, or DS-R1-671B) sufficient for it, guided by entity- and confidence-aware features. Trained via behavior cloning followed by PPO under a five-component cost-aware reward (final, cumulative, step-wise, cost, balance) and coupled with an explicit two-tier termination policy (5-hop cap plus a tau=0.3 confidence gate), the agent carves a Pareto-optimal efficiency frontier. On HotpotQA it cuts Monetary Inference Cost by 60.07% against IRCoT with only a 3.7% F1 drop; it matches Adaptive-RAG’s F1 at 37.30% lower cost; and it attains up to 2.33x higher Quality-per-Monetary-Cost. Consistent gains on MuSiQue, 2WikiMultiHopQA, CRAG, and Bamboogle confirm strong out-of-distribution robustness, setting a new paradigm for fine-grained resource control in multi-hop RAG.
%R 10.18653/v1/2026.acl-long.1562
%U https://aclanthology.org/2026.acl-long.1562/
%U https://doi.org/10.18653/v1/2026.acl-long.1562
%P 33883-33904

Markdown (Informal)

[RAG-on-a-Diet: A Reinforcement Learning-Based Dynamic Resource Optimization Framework for RAG](https://aclanthology.org/2026.acl-long.1562/) (Ding & Zhao, ACL 2026)

• RAG-on-a-Diet: A Reinforcement Learning-Based Dynamic Resource Optimization Framework for RAG (Ding & Zhao, ACL 2026)

ACL

• Hongwen Ding and Yizheng Zhao. 2026. RAG-on-a-Diet: A Reinforcement Learning-Based Dynamic Resource Optimization Framework for RAG. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 33883–33904, San Diego, California, United States. Association for Computational Linguistics.