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The proposed Mixture of Roles (MoRe), which adaptively composes multiple specializations into a single steering vector for single-turn inference, enables multi-perspective specialization in a single-agent, single-turn inference process.
This work proposes a new approach that quantifies the effectiveness of each interaction by the reduction in the assistant's uncertainty, measured via entropy over recommendations, to fine-tune the LLM, enabling strategic interaction generation.
This paper proposes World Model RL (WMRL), which replaces environment execution with a world model to remove this bottleneck and accelerates training by 3-4x on various tasks at different agent scales, while exceeding the performance of standard RL baselines.
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