Continual Model Merging Approach Uses ODE Perspective to Address Forgetting
A recent study published on arXiv (2605.19409) introduces a continual model merging (CMM) technique viewed through the lens of ordinary differential equations (ODEs) to facilitate swift adaptation of foundation models to tasks that arrive sequentially. Current merging strategies fail to provide clear control over how learning capacity is distributed between established skills and newly integrated models, resulting in significant forgetting, particularly when task significance varies. The researchers contend that earlier approaches regard each task model as a separate parameter point and utilize static algebraic combinations, neglecting the need for a transition that honors the connections between independently trained models in parameter space. They posit that effective merged models exist along low-loss connecting pathways, inspired by mode connectivity.
Key facts
- Paper arXiv:2605.19409 proposes continual model merging (CMM) from an ODE perspective.
- CMM enables rapid customization of foundation models across sequentially arriving tasks.
- Existing merging rules lack explicit controllability over learning capacity allocation.
- Deficiency accumulates into severe forgetting in heterogeneous task importance scenarios.
- Previous methods treat each task model as an isolated parameter point.
- Previous methods apply fixed algebraic combinations instead of constructing transitions.
- Approach is motivated by mode connectivity.
- Desirable merged models are assumed to lie on low-loss connecting paths.
Entities
Institutions
- arXiv