DeepEyesV2: Toward Agentic Multimodal Model
Agentic multimodal models should not only comprehend text and images, but
also actively invoke external tools, such as code execution environments and
web search, and integrate these operations into reasoning. In this work, we
introduce DeepEyesV2 and explore how to build an agentic multimodal model from
the perspectives of data construction, training methods, and model evaluation.
We observe that direct reinforcement learning alone fails to induce robust
tool-use behavior. This phenomenon motivates a two-stage training pipeline: a
cold-start stage to establish tool-use patterns, and reinforcement learning
stage to further refine tool invocation. We curate a diverse, moderately
challenging training dataset, specifically including examples where tool use is
beneficial. We further introduce RealX-Bench, a comprehensive benchmark
designed to evaluate real-world multimodal reasoning, which inherently requires
the integration of multiple capabilities, including perception, search, and
reasoning. We evaluate DeepEyesV2 on RealX-Bench and other representative
benchmarks, demonstrating its effectiveness across real-world understanding,
mathematical reasoning, and search-intensive tasks. Moreover, DeepEyesV2
exhibits task-adaptive tool invocation, tending to use image operations for
perception tasks and numerical computations for reasoning tasks. Reinforcement
learning further enables complex tool combinations and allows model to
selectively invoke tools based on context. We hope our study can provide
guidance for community in developing agentic multimodal models.
69114e31830c57fd4c795fa3
Jack Hong
69114e31830c57fd4c795fa4
Chenxiao Zhao
admin_assigned
2026-02-17T17:16:58.903Z
63fc5b724c57549ad5e54558
/avatars/1374c1e8969533dd7543959666f16d1a.svg
Chenxiao Zhao
ChenShawn
user
ChenShawn
69114e31830c57fd4c795fa5
ChengLin Zhu
69114e31830c57fd4c795fa6
Weiheng Lu
69114e31830c57fd4c795fa7
Guohai Xu
69114e31830c57fd4c795fa8
Xing Yu
DeepEyesV2: Toward Agentic Multimodal Model
Agentic multimodal models should not only comprehend text and images, but
also actively invoke external tools, such as code execution environments and
web search, and integrate these operations into reasoning. In this work, we
introduce DeepEyesV2 and explore how to build an agentic multimodal model from
the perspectives of data construction, training methods, and model evaluation.
We observe that direct reinforcement learning alone fails to induce robust
tool-use behavior. This phenomenon motivates a two-stage training pipeline: a
cold-start stage to establish tool-use patterns, and reinforcement learning
stage to further refine tool invocation. We curate a diverse, moderately
challenging training dataset, specifically including examples where tool use is
beneficial. We further introduce RealX-Bench, a comprehensive benchmark
designed to evaluate real-world multimodal reasoning, which inherently requires
the integration of multiple capabilities, including perception, search, and
reasoning. We evaluate DeepEyesV2 on RealX-Bench and other representative
benchmarks, demonstrating its effectiveness across real-world understanding,
mathematical reasoning, and search-intensive tasks. Moreover, DeepEyesV2
exhibits task-adaptive tool invocation, tending to use image operations for
perception tasks and numerical computations for reasoning tasks. Reinforcement
learning further enables complex tool combinations and allows model to
selectively invoke tools based on context. We hope our study can provide
guidance for community in developing agentic multimodal models.
DeepEyesV2, an agentic multimodal model, uses a two-stage training pipeline to effectively integrate tool use, demonstrating robust performance across real-world reasoning tasks.
DeepEyesV2 reinforcement learning cold-start stage RealX-Bench multimodal reasoning tool invocation task-adaptive tool combinations