Even Small Reasoners Should Quote Their Sources: Introducing the
  Pleias-RAG Model Family
Authors: name: Pierre-Carl Langlais, name: Pavel Chizhov, name: Mattia Nee, name: Carlos Rosas Hinostroza, name: Matthieu Delsart, name: Irène Girard, name: Othman Hicheur, name: Anastasia Stasenko, name: Ivan P. Yamshchikov

We introduce a new generation of small reasoning models for RAG, search, and
source summarization. Pleias-RAG-350m and Pleias-RAG-1B are mid-trained on a
large synthetic dataset emulating the retrieval of a wide variety of
multilingual open sources from the Common Corpus. They provide native support
for citation and grounding with literal quotes and reintegrate multiple
features associated with RAG workflows, such as query routing, query
reformulation, and source reranking. Pleias-RAG-350m and Pleias-RAG-1B
outperform SLMs below 4 billion parameters on standardized RAG benchmarks
(HotPotQA, 2wiki) and are competitive with popular larger models, including
Qwen-2.5-7B, Llama-3.1-8B, and Gemma-3-4B. They are the only SLMs to date
maintaining consistent RAG performance across leading European languages and
ensuring systematic reference grounding for statements. Due to their size and
ease of deployment on constrained infrastructure and higher factuality by
design, the models unlock a range of new use cases for generative AI.