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Enhancing on-policy reinforcement learning can paradoxically reduce the diversity of successful behaviors, leading to a trade-off that challenges future trainability.
OPD-Evolver outperforms traditional memory systems by up to 11.5%, showcasing a new paradigm in agent evolution that transcends mere memory storage.
Language models are increasingly doing their real work in the "invisible" latent space, not the tokens we see.
