💡 The paper introduces HyperEyes, a parallel multimodal search agent designed to optimize inference efficiency through dual-grained reinforcement learning. Existing multimodal search agents process target entities sequentially, which can lead to redundant interaction rounds and decreased efficiency. HyperEyes addresses this issue by fusing visual grounding and retrieval into a single atomic action, enabling concurrent search across multiple entities.

The HyperEyes system is trained in two stages. The first stage involves a Parallel-Amenable Data Synthesis Pipeline that generates efficiency-oriented trajectories via Progressive Rejection Sampling. The second stage utilizes a Dual-Grained Efficiency-Aware Reinforcement Learning framework, which operates at two levels. At the macro level, the framework uses a trajectory-level reward called TRACE, which is designed to suppress superfluous tool calls without restricting genuine multi-hop search. At the micro level, the framework adapts On-Policy Distillation to inject dense token-level corrective signals from an external teacher on failed rollouts, mitigating the credit-assignment deficiency of sparse outcome rewards.

The paper also introduces a new benchmark called IMEB, which jointly evaluates search capability and efficiency. The results show that HyperEyes surpasses the strongest comparable open-source agent by 9.9% in accuracy, while using 5.3x fewer tool-call rounds on average. The HyperEyes system demonstrates the effectiveness of dual-grained reinforcement learning in optimizing inference efficiency and achieving better search results. Overall, the paper contributes to the development of more efficient and effective multimodal search agents.