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This paper proposes PRISM, a multi-reward RL framework that decomposes policy space rather than mixing rewards, improving multi-reward optimization and enabling inference-time controllability. Experiments on reasoning and alignment tasks show it outperforms existing baselines.
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.