ARBOR. ARBOR（Adaptive Rubric Buffer for Online Reward）是一个面向 search agent RL 训练的可复用 process reward 框架。核心思路是：在 RL 训练过程中按 category 维护一份 rubric memory，从 query-group 内的对比轨迹中诱导 query-local draft rubric，经 admission → consolidation → retirement 生命周期沉淀为 category-level 的 common rubric，在 outcome-only reward 信号坍缩时仍能提供组内过程区分。

Problem

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Audience

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Market

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Pricing

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