Unlocking the Potential of Continual Model Merging: An ODE Perspective
Continual Model Merging (CMM) enables rapid customization of foundation models by sequentially incorporating task-adapted models without repeated retraining. However, existing merging rules usually update the deployed model through fixed algebraic or projection-based operations, providing limited control over how much previously accumulated knowledge should be retained relative to the incoming task model. This limitation leads to unstable retention and performance degradation in long task streams, and becomes more pronounced when tasks have heterogeneous utilities. We propose ODE-driven Merging (ODE-M), a controllable framework that formulates each continual merge as a trajectory in param...
| Authors | Lihong Lin, Haidong Kang |
|---|---|
| Primary category | cs.LG |
| Published | 19 May 2026 |
| Last updated at source | 23 May 2026 |
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