DEEPRUBRIC: Evidence-Tree Rubric Supervision for Efficient Reinforcement Learning of Deep Research Agents

ACL ARR 2026 May Submission14109 Authors

Keywords: deep research agents, rubric-based reinforcement learning, retrieval-augmented generation

Abstract:

Deep research agents synthesize long-form reports by searching and reasoning over retrieved evidence.
Reinforcement learning with rubric-based rewards improves these agents by optimizing them against checkable criteria that translate report quality into reward signals, but its efficiency depends on whether those criteria reliably capture the task scope and evidence needs.
Most existing studies ask an LLM to generate rubrics for a given query, but when the model fails to infer the underlying information needs, the generated rubrics may be incomplete and reduce RL efficiency.
To obtain more reliable query--rubric supervision, we introduce DeepRubric, a data construction framework that reverses this process: instead of inferring evaluation criteria for a given query, it first determines what an evidence-backed report should be evaluated on and then synthesizes aligned query--rubric pairs from those evaluation targets.
Starting from a sampled seed topic, DeepRubric builds an evidence tree by recursively expanding evidence-backed sub-questions, whose leaves serve as atomic and verifiable evaluation targets.
It then uses the evidence tree to synthesize the training query and rubrics, ensuring that the reward evaluates exactly the information requested by the query.
Using DeepRubric, we construct 9K query--rubric supervision examples and train DeepRubric-8B with rubric-based GRPO, achieving comparable performance to prior open state-of-the-art deep research models across three benchmarks with roughly 13$\times$ fewer RL GPU-hours.

Paper Type: Long

Research Area: LLM agents

Research Area Keywords: reinforcement learning in agents; planning in agents; tool use;

Contribution Types: Approaches to low-compute settings (efficiency), Publicly available software and/or pre-trained models

Languages Studied: English

EMNLP 2026 AI Reviewing Experiment: yes

Submission Number: 14109