Amortized Low-Rank Adaptation for Model-Based Reinforcement Learning
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 (Context-conditioned Low-rank Adaptation of World models), which addresses this tradeoff by using a ...
| Authors | Fernando Palafox, David Fridovich-Keil |
|---|---|
| Primary category | cs.LG |
| Published | 10 September 2026 |
| Last updated at source | 21 September 2026 |
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