The advent of agentic multimodal models has empowered systems to actively interact with external environments. However, current agents suffer from a profound meta-cognitive deficit: they struggle to arbitrate between leveraging internal knowledge and querying external utilities. Consequently, they frequently fall prey to blind tool invocation, resorting to reflexive tool execution even when queries are resolvable from the raw visual context. This pathological behavior precipitates severe latency bottlenecks and injects extraneous noise that derails sound reasoning. Existing reinforcement learning protocols attempt to mitigate this via a scalarized reward that penalizes tool usage. Yet, this coupled formulation creates an irreconcilable optimization dilemma: an aggressive penalty suppresses essential tool use, whereas a mild penalty is entirely subsumed by the variance of the accuracy reward during advantage normalization, rendering it impotent against tool overuse. To transcend this bottleneck, we propose HDPO, a framework that reframes tool efficiency from a competing scalar objective to a strictly conditional one. By eschewing reward scalarization, HDPO maintains two orthogonal optimization channels: an accuracy channel that maximizes task correctness, and an efficiency channel that enforces execution economy exclusively within accurate trajectories via conditional advantage estimation. This decoupled architecture naturally induces a cognitive curriculum-compelling the agent to first master task resolution before refining its self-reliance. Extensive evaluations demonstrate that our resulting model, Metis, reduces tool invocations by orders of magnitude while simultaneously elevating reasoning accuracy.

翻译：智能多模态模型的兴起赋予了系统与外部环境主动交互的能力。然而，当前模型存在深刻的元认知缺陷：它们难以在利用内部知识与查询外部工具之间进行有效仲裁。因此，即使面对可从原始视觉语境中解决的查询，模型也常陷入盲目调用工具的陷阱，采取反射性的工具执行行为。这种病态行为会导致严重的延迟瓶颈，并引入干扰正确推理的额外噪声。现有的强化学习方法试图通过标量化奖励惩罚工具使用来缓解这一问题。然而，这种耦合的优化目标造成了不可调和的困境：激进的惩罚会抑制必要的工具使用，而温和的惩罚在优势归一化过程中完全被准确率奖励的方差所淹没，无法有效遏制工具过度使用。为突破这一瓶颈，我们提出HDPO框架，将工具效率从竞争性标量目标重新定义为严格的条件性目标。通过摒弃奖励标量化，HDPO维持两个正交的优化通道：一个最大化任务正确性的准确率通道，以及一个通过条件优势估计在准确轨迹内强制执行经济性的效率通道。这种解耦架构自然产生认知课程——迫使智能体在完善自我依赖能力之前先掌握任务解决能力。大量实验表明，我们的模型Metis在显著提升推理准确率的同时，将工具调用次数减少数个数量级。