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Multi-agent reinforcement learning (MARL) provides a powerful framework for learning coordinated behaviors through interactions with the environment. Developing MARL policies requires balancing expressive modeling of complex and multimodal action distributions with efficient training and execution. Generative policies,...
FlexLoop is proposed, a novel post-training framework that converts pretrained fixed-depth looped policies into depth-elastic policies that supports reliable inference across recurrent depths and enables state-wise adaptive inference through recurrent-depth consistency.
The proposed Module Level Reward Evolution Framework integrates three mechanisms: reflection-based refinement, hybrid credit assignment, and a merge strategy with rollback, which together improve the effectiveness and robustness of reward optimization.
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