On-Policy Merge Stabilizes Multi-Task Reinforcement Learning
One on-policy RL trainer per domain, merged at the KL barycenter of their policies whenever one drifts too far. Why the merge decouples domains and stabilizes multi-task RL, what it costs, and how it compares with multi-task RL, cascade RL, merging after training and MOPD.
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