Deep Research as Rubric

Open-ended reasoning and long-form generation tasks lack reliable automatic verification signals for reward-based policy optimization. Rubrics offer a promising alternative, but existing approaches treat them as given artifacts—either hand-crafted or prompt-generated—and often miss the task-specific, knowledge-intensive dimensions that matter most, distorting the reward signal. Our key observation is that *rubric construction is itself a research problem*: identifying what makes a response correct or insightful requires discovering and synthesizing external knowledge. We propose **Deep Research as Rubric (DR-rubric-8B)**, a two-stage framework for constructing such rubrics. Stage I elicits domain facts, structural constraints, and failure modes through iterative multi-turn agentic search; Stage II distills this evidence into atomic, independently verifiable constraints for GRPO-based policy optimization. Because the model under training can serve as its own rubric generator, DR-rubric-8B supports *bootstrap rubric generation* without frontier-model assistance. We evaluate on 6 benchmarks spanning agentic research and expert reasoning. Experiments show that DR-Rubric achieves strong competitive performance with only 1K–3K training instances, where GPT-5-generated rubrics particularly benefit breadth coverage on agentic tasks, while bootstrap rubrics exhibit a specialization-to-rebalancing evolution and achieve the best overall performance at the third iteration. Results demonstrate that reframing rubric construction from static evaluation templates into an evidence-driven research process yields more scalable, fine-grained reward signals for open-ended tasks.