  We present COmpetitive Mechanisms for Efficient Transfer (COMET), a modular
world model which leverages reusable, independent mechanisms across different
environments. COMET is trained on multiple environments with varying dynamics
via a two-step process: competition and composition. This enables the model to
recognise and learn transferable mechanisms. Specifically, in the competition
phase, COMET is trained with a winner-takes-all gradient allocation,
encouraging the emergence of independent mechanisms. These are then re-used in
the composition phase, where COMET learns to re-compose learnt mechanisms in
ways that capture the dynamics of intervened environments. In so doing, COMET
explicitly reuses prior knowledge, enabling efficient and interpretable
adaptation. We evaluate COMET on environments with image-based observations. In
contrast to competitive baselines, we demonstrate that COMET captures
recognisable mechanisms without supervision. Moreover, we show that COMET is
able to adapt to new environments with varying numbers of objects with improved
sample efficiency compared to more conventional finetuning approaches.
