ReForge: Refining Merged Models with Anchor-Regularized Regression

תקציר מקורי באנגליתarXiv:2605.12843v3 Announce Type: replace-cross Abstract: Model merging aims to combine multiple task-specific expert models into a single model without joint retraining, offering a practical alternative to multi-task learning when data access or computational budget is limited. Existing model merging methods rarely exploit strong merged models as priors for further improvement. To address this limitation, we propose ReForge, a bilevel optimization framework that formulates module-wise refinement as Bayesian linear regression with an anchor-centered prior. The inner level yields a closed-form MAP estimate from unlabeled calibration activations. The outer level uses Bayesian optimization to jointly select heterogeneous regularization strengths and assembly scales using held-out validation d