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CoDrift is a compositional framework for offline reinforcement learning that combines behavioral and value objectives into a unified policy field, achieving state-of-the-art performance on benchmarks like OGBench and D4RL.
GenPO++ proposes a reversible generative policy optimization framework that uses history states as auxiliary memory in a high-order reversible ODE solver, enabling exact inversion and Jacobian-free likelihood-ratio computation for flow-based policies in reinforcement learning. It achieves competitive performance on large-scale control, fine-tuning, and real-world robotic tasks while improving stability and efficiency.