Summary
RubricEM (arXiv:2505.07228) addresses training deep research agents—systems that plan, search, evaluate evidence, and synthesize long-form reports—where standard reinforcement learning struggles because outputs lack ground-truth answers and trajectories span many tool-augmented decisions. The authors propose that rubrics should serve not merely as final-answer evaluators but as a shared interface for learning. The method uses rubric-guided policy decomposition combined with meta-reinforcement learning, providing a mechanism for turning past attempts into reusable experience, pushing RL beyond the regime of verifiable rewards. Authored by Gaotang Li, Bhavana Dalvi Mishra, and Zifeng Wang, the paper was released on May 9, 2025 in the NLP domain. This post is an aggregated forum entry from zhichai.net introducing the paper's abstract and arXiv link.
Paper Overview
Field: NLP
Authors: Gaotang Li, Bhavana Dalvi Mishra, Zifeng Wang
Published: 2025-05-09
arXiv: 2505.07228
Abstract
Training deep research agents—systems that plan, search, evaluate evidence, and synthesize long-form reports—pushes reinforcement learning beyond the regime of verifiable rewards. Their outputs lack ground-truth answers, their trajectories span many tool-augmented decisions, and standard post-training offers little mechanism for turning past attempts into reusable experience.
In this work, the authors argue that rubrics should serve not merely as final-answer evaluators, but as the shared interface for learning. The proposed approach, RubricEM, combines meta-reinforcement learning with rubric-guided policy decomposition, enabling agents to convert past attempts into reusable experience even in settings without verifiable rewards.
Links
- Paper: https://arxiv.org/abs/2505.07228
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