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RoRo introduces a rubric-guided process reward framework for stepwise model routing in Large Reasoning Models, using process rewards alongside outcome rewards to train a routing policy via GRPO, outperforming baselines on reasoning benchmarks.
Co-ReAct introduces a rubric-guided action-selection framework that uses rubrics as step-level guidance during inference for ReAct agents, improving trajectory quality and outperforming baselines on DeepResearchBench and SQA-CS-V2.
This paper introduces RubricEM, a reinforcement learning framework that uses rubric-guided policy decomposition and reflection-based meta-policy evolution to train deep research agents for long-form tasks. The resulting RubricEM-8B model demonstrates strong performance on long-form research benchmarks by leveraging stage-aware planning and denser semantic feedback.