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University of Science and Technology
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Large-scale rewards can unfairly suppress learning signals from smaller rewards in multi-objective RL; CorrGRPO fixes this by normalizing reward covariances.
Co-evolution in agentic systems could unlock unprecedented levels of adaptability, allowing agents to evolve beyond human-imposed limitations.
