MMSearch-R1: Incentivizing LMMs to Search
Authors: name: Jinming Wu, name: Zihao Deng, name: Wei Li, name: Yiding Liu, name: Bo You, name: Bo Li, name: Zejun Ma, name: Ziwei Liu

Robust deployment of large multimodal models (LMMs) in real-world scenarios
requires access to external knowledge sources, given the complexity and dynamic
nature of real-world information. Existing approaches such as
retrieval-augmented generation (RAG) and prompt engineered search agents rely
on rigid pipelines, often leading to inefficient or excessive search behaviors.
We present MMSearch-R1, the first end-to-end reinforcement learning framework
that enables LMMs to perform on-demand, multi-turn search in real-world
Internet environments. Our framework integrates both image and text search
tools, allowing the model to reason about when and how to invoke them guided by
an outcome-based reward with a search penalty. To support training, We collect
a multimodal search VQA dataset through a semi-automated pipeline that covers
diverse visual and textual knowledge needs and curate a search-balanced subset
with both search-required and search-free samples, which proves essential for
shaping efficient and on-demand search behavior. Extensive experiments on
knowledge-intensive and info-seeking VQA tasks show that our model not only
outperforms RAG-based baselines of the same model size, but also matches the
performance of a larger RAG-based model while reducing search calls by over
30%. We further analyze key empirical findings to offer actionable insights for
advancing research in multimodal search.