@inproceedings{xu-etal-2026-ring,
title = "{RING}: Retrieval-Internalized Generation for Continual Large-Scale Knowledge Injection",
author = "Xu, Shicheng and
Pang, Liang and
Chen, Liyi and
Wei, Zihao and
Deng, Jingcheng and
Gao, Yan and
Wu, Yi and
Hu, Yao and
Shen, Huawei and
Cheng, Xueqi",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1940/",
pages = "41365--41381",
ISBN = "979-8-89176-462-0",
abstract = "Retrieval-augmented generation (RAG) improves factuality but adds latency and engineering overhead at serving time. We propose RING (Retrieval-Internalized Generation), a {holistic paradigm spanning both architecture and training} that injects large-scale external knowledge into a Mixture-of-Memory Experts and learns parametric search over this internal memory via reinforcement learning, removing the external retriever entirely. Training proceeds in three stages: continued pre-training injects new corpora into a Knowledge Expert via our novel \textit{Dual Causal Attention}; supervised fine-tuning teaches a ``search-then-answer'' pattern; and reinforcement learning with hierarchical rewards optimizes the routing-and-search policy over the parametric memory. Unlike prior parametric injection methods that pair internal memory with a fixed or rule-based retriever, RING learns its retrieval policy directly from task signals. We further frame RING theoretically as a search-free approximation to the classical RAG objective. To evaluate large-scale injection of genuinely new knowledge without test-time leakage, we further construct News-2025, a benchmark built from news strictly post-dating the base LLM{'}s pretraining cutoff. RING matches or surpasses both search-based RAG and parametric injection baselines in accuracy and efficiency."
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<abstract>Retrieval-augmented generation (RAG) improves factuality but adds latency and engineering overhead at serving time. We propose RING (Retrieval-Internalized Generation), a holistic paradigm spanning both architecture and training that injects large-scale external knowledge into a Mixture-of-Memory Experts and learns parametric search over this internal memory via reinforcement learning, removing the external retriever entirely. Training proceeds in three stages: continued pre-training injects new corpora into a Knowledge Expert via our novel Dual Causal Attention; supervised fine-tuning teaches a “search-then-answer” pattern; and reinforcement learning with hierarchical rewards optimizes the routing-and-search policy over the parametric memory. Unlike prior parametric injection methods that pair internal memory with a fixed or rule-based retriever, RING learns its retrieval policy directly from task signals. We further frame RING theoretically as a search-free approximation to the classical RAG objective. To evaluate large-scale injection of genuinely new knowledge without test-time leakage, we further construct News-2025, a benchmark built from news strictly post-dating the base LLM’s pretraining cutoff. RING matches or surpasses both search-based RAG and parametric injection baselines in accuracy and efficiency.</abstract>
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%0 Conference Proceedings
%T RING: Retrieval-Internalized Generation for Continual Large-Scale Knowledge Injection
%A Xu, Shicheng
%A Pang, Liang
%A Chen, Liyi
%A Wei, Zihao
%A Deng, Jingcheng
%A Gao, Yan
%A Wu, Yi
%A Hu, Yao
%A Shen, Huawei
%A Cheng, Xueqi
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F xu-etal-2026-ring
%X Retrieval-augmented generation (RAG) improves factuality but adds latency and engineering overhead at serving time. We propose RING (Retrieval-Internalized Generation), a holistic paradigm spanning both architecture and training that injects large-scale external knowledge into a Mixture-of-Memory Experts and learns parametric search over this internal memory via reinforcement learning, removing the external retriever entirely. Training proceeds in three stages: continued pre-training injects new corpora into a Knowledge Expert via our novel Dual Causal Attention; supervised fine-tuning teaches a “search-then-answer” pattern; and reinforcement learning with hierarchical rewards optimizes the routing-and-search policy over the parametric memory. Unlike prior parametric injection methods that pair internal memory with a fixed or rule-based retriever, RING learns its retrieval policy directly from task signals. We further frame RING theoretically as a search-free approximation to the classical RAG objective. To evaluate large-scale injection of genuinely new knowledge without test-time leakage, we further construct News-2025, a benchmark built from news strictly post-dating the base LLM’s pretraining cutoff. RING matches or surpasses both search-based RAG and parametric injection baselines in accuracy and efficiency.
%U https://aclanthology.org/2026.findings-emnlp.1940/
%P 41365-41381
Markdown (Informal)
[RING: Retrieval-Internalized Generation for Continual Large-Scale Knowledge Injection](https://aclanthology.org/2026.findings-emnlp.1940/) (Xu et al., Findings 2026)
ACL
- Shicheng Xu, Liang Pang, Liyi Chen, Zihao Wei, Jingcheng Deng, Yan Gao, Yi Wu, Yao Hu, Huawei Shen, and Xueqi Cheng. 2026. RING: Retrieval-Internalized Generation for Continual Large-Scale Knowledge Injection. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 41365–41381, Budapest, Hungary. Association for Computational Linguistics.