@inproceedings{ji-etal-2026-evo,
title = "{EVO}-{RAG}: Evolving Retrieval-Augmented Agents for Efficient Multi-Hop Query Optimization",
author = "Ji, Yuelyu and
Meng, Rui and
Li, Zhuochun and
He, Daqing",
editor = "Wang, Yimu and
Choi, Yee Man and
Wu, Di and
Ouyang, Siqi and
Azadani, Mozhgan Nasr and
Fung, Yi R. (May)",
booktitle = "Proceedings of the 1st Workshop on Grounding Language Models: Learning Faithfully and Efficiently ({G}round{LM} 2026)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.groundlm-1.6/",
pages = "72--83",
ISBN = "979-8-89176-478-1",
abstract = "Retrieval-augmented generation (RAG) grounds large language models (LLMs) in external evidence, yet multi-hop pipelines still suffer from redundant sub-queries, shallow exploration, and premature or delayed stopping. We present EVO-RAG, a phase-aware framework that couples a lightweight two-stage curriculum (Discovery{\textrightarrow}Refinement) with seven step-level rewards and an in-episode time scheduler. The scheduler decays exploration incentives as evidence accumulates while increasing efficiency and correctness pressure as uncertainty shrinks. Beyond scalar rewards, we train a multi-head preference model and benchmark DPO, PPO, and GRPO under identical rollouts and curricula for a controlled comparison. Evaluated on HotpotQA, 2WikiMultiHopQA, MuSiQue, and Bamboogle with 8B-class backbones, EVO-RAG improves EM/F1 while reducing redundant hops. Ablations show that suppressing query overlap, rewarding controlled backtracking and justified refusal, and time-dynamic weighting are key to the accuracy{--}efficiency trade-off."
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<abstract>Retrieval-augmented generation (RAG) grounds large language models (LLMs) in external evidence, yet multi-hop pipelines still suffer from redundant sub-queries, shallow exploration, and premature or delayed stopping. We present EVO-RAG, a phase-aware framework that couples a lightweight two-stage curriculum (Discovery→Refinement) with seven step-level rewards and an in-episode time scheduler. The scheduler decays exploration incentives as evidence accumulates while increasing efficiency and correctness pressure as uncertainty shrinks. Beyond scalar rewards, we train a multi-head preference model and benchmark DPO, PPO, and GRPO under identical rollouts and curricula for a controlled comparison. Evaluated on HotpotQA, 2WikiMultiHopQA, MuSiQue, and Bamboogle with 8B-class backbones, EVO-RAG improves EM/F1 while reducing redundant hops. Ablations show that suppressing query overlap, rewarding controlled backtracking and justified refusal, and time-dynamic weighting are key to the accuracy–efficiency trade-off.</abstract>
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%0 Conference Proceedings
%T EVO-RAG: Evolving Retrieval-Augmented Agents for Efficient Multi-Hop Query Optimization
%A Ji, Yuelyu
%A Meng, Rui
%A Li, Zhuochun
%A He, Daqing
%Y Wang, Yimu
%Y Choi, Yee Man
%Y Wu, Di
%Y Ouyang, Siqi
%Y Azadani, Mozhgan Nasr
%Y Fung, Yi R. (May)
%S Proceedings of the 1st Workshop on Grounding Language Models: Learning Faithfully and Efficiently (GroundLM 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-478-1
%F ji-etal-2026-evo
%X Retrieval-augmented generation (RAG) grounds large language models (LLMs) in external evidence, yet multi-hop pipelines still suffer from redundant sub-queries, shallow exploration, and premature or delayed stopping. We present EVO-RAG, a phase-aware framework that couples a lightweight two-stage curriculum (Discovery→Refinement) with seven step-level rewards and an in-episode time scheduler. The scheduler decays exploration incentives as evidence accumulates while increasing efficiency and correctness pressure as uncertainty shrinks. Beyond scalar rewards, we train a multi-head preference model and benchmark DPO, PPO, and GRPO under identical rollouts and curricula for a controlled comparison. Evaluated on HotpotQA, 2WikiMultiHopQA, MuSiQue, and Bamboogle with 8B-class backbones, EVO-RAG improves EM/F1 while reducing redundant hops. Ablations show that suppressing query overlap, rewarding controlled backtracking and justified refusal, and time-dynamic weighting are key to the accuracy–efficiency trade-off.
%U https://aclanthology.org/2026.groundlm-1.6/
%P 72-83
Markdown (Informal)
[EVO-RAG: Evolving Retrieval-Augmented Agents for Efficient Multi-Hop Query Optimization](https://aclanthology.org/2026.groundlm-1.6/) (Ji et al., GroundLM 2026)
ACL