Deep Research as Rubric for Reinforcement Learning

• URL: http://arxiv.org/abs/2606.01091v1
• Date: Sun, 31 May 2026 08:25:04 GMT
• Title: Deep Research as Rubric for Reinforcement Learning
• Abstract summary: We propose a two-stage framework for constructing open-ended rubrics.<n>DR-rubric-8B supports bootstrap generation without frontier-model assistance.<n>Experiments show that DR-rubric achieves strong competitive performance with only 1K -- 3K training instances.
• Score: 35.642522713903794
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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), 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, Gemini-generated rubrics yield the most balanced performance across agentic and expert reasoning tasks, and bootstrap rubrics exhibit a specialization-to-rebalancing evolution achieving 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.

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