SOURCE-LINKED INTELLIGENCE

Amortized Low-Rank Adaptation for Model-Based Reinforcement Learning

arXiv · AI, language, vision and robotics · article · Sep 10, 2026 · UTC

World models let agents plan by predicting the consequences of their actions, but changes in the environment can make them inaccurate. We study the problem of adapting a world model to an unknown test-time environment, drawn from a known environment family, using only a few episodes of interaction. Existing approaches trade off computational cost against expressivity, i.e., the range of models a method can produce. For example, in-context learning is computationally cheap but limited in expressivity, and gradient-based adaptation is expressive but computationally expensive. We present CLAW (Co

Read original source ↗ Open in workspace

recordType

paper

region

Global