DR-MMSearchAgent: Deepening Reasoning in Multimodal Search Agents
Agentic multimodal models have garnered significant attention for their ability to leverage external tools to tackle complex tasks. However, it is observed that such agents often meet premature interaction collapse, caused by two primary reasons: 1) the terminal reward often appending on the last token prevents the advantage from distinguishing trajectories with exploratory behavior; 2) excessively redundant context hinders the agent from absorbing useful feedback. To address these issues, we propose the Deepening Reasoning MMSearchAgent, the framework leverages the structural proximity to derive advantage signals from the whole rollout trajectories in an entire batch, such that trajector...
| Authors | Shengqin Wang, Wentao Yan, Huichi Zhou et al. |
|---|---|
| Primary category | cs.CV |
| Published | 21 April 2026 |
| Last updated at source | 21 April 2026 |
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