aiqbench analysis
RING proposes a novel architecture called Mixture-of-Memory Experts that internalizes retrieval logic through reinforcement learning, effectively eliminating the need for external search tools during inference. By training on fresh news data via News-2025 and utilizing Dual Causal Attention, this method achieves accuracy comparable to traditional RAG while significantly reducing latency by removing serving-time overhead. The approach represents a significant theoretical shift from hybrid retrieval-generation models toward fully parametric knowledge injection that learns its own search policies directly from task signals.
RING: Retrieval-Internalized Generation for Continual Large-Scale Knowledge Injection
arXiv:2608.01630v1 Announce Type: cross 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 \textit{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.
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 \textit{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.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2608.01630 [cs.CL]
(or arXiv:2608.01630v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.01630 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 270 words
Related coverage
Academia is for Ambition — Alex Zhang, MIT
Latent Space · Oct 2, 2026
[AINews] not much happened today
Latent Space · Sep 10, 2026
The generative AI customization spectrum: From prompt engineering to custom models on AWS
AWS · Sep 14, 2026
Modernizing and scaling support operations with generative AI on AWS
AWS · Sep 2, 2026