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Research questionHow can agents choose task-dependent world-model rollout horizons for effective multi-step planning?Learned world models let agents imagine future environmental states without interacting with real environments. Single-step or fixed-horizon imagination may provide insufficient foresight when tasks require different amounts of planning and progress changes over time.
AI
AI Agents
Machine Learning
Reasoning
Reinforcement Learning
Latest papersRecent research connected to this question, newest first.Imagine-then-Plan: Agent Learning from Adaptive Lookahead with World ModelsApplies to agents that use learned world models to generate imagined future trajectories without accessing real environments. The source studies training-free and reinforcement-trained variants and reports results on representative agent benchmarks; broader deployment conditions are not specified.research paper · Sep 3, 2026
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