AI RESEARCH
Mind Dreamer: Untethering Imagination via Active Latent Intervention on Latent Manifolds
arXiv CS.LG
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ArXi:2605.16030v1 Announce Type: new Model-Based Reinforcement Learning (MBRL) leverages latent imagination for sample efficiency, yet remains constrained by Historical Tethering: imagination is typically initialized from observed states. This creates a learning asymmetry, where the world model's manifold discovery outpaces the policy's sparse-reward optimization. We propose Mind Dreamer (MD), a framework that operationalizes Active Latent Intervention (ALI) to transcend Markovian continuity.