Bayesian Model Merging

• URL: http://arxiv.org/abs/2605.12843v1
• Date: Wed, 13 May 2026 00:36:47 GMT
• Title: Bayesian Model Merging
• Abstract summary: Model merging aims to combine multiple task-specific expert models into a single model without joint retraining.<n>This paper introduces Bayesian Model Merging (BMM), a plug-and-play bi-level optimization framework.<n>BMM consistently outperforms all plug-and-play anchor baselines.
• Score: 17.887004278413915
• License: http://creativecommons.org/licenses/by/4.0/
• 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 methods, however, face two key limitations: (1) they overlook the valuable inductive bias of strong anchor models and estimate the merged weights from scratch, and (2) they rely on a shared hyperparameter setting across different modules of the network, lacking a global optimization strategy. This paper introduces Bayesian Model Merging (BMM), a plug-and-play bi-level optimization framework, where the inner level formulates the model merging as an activation-based Bayesian regression under a strong prior induced by an anchor model, yielding an efficient closed-form solution; and the outer level leverages a Bayesian optimization procedure to search module-specific hyperparameters globally based on a small validation set. Furthermore, we reveal a key alignment between activation statistics and task vectors, enabling us to derive a data-free variant of BMM that estimates the Gram matrix for regression without any auxiliary data. Across extensive benchmarks, including up to 20-task merging in vision and 5-task merging in language, BMM consistently outperforms all plug-and-play anchor baselines (e.g., TA, WUDI-Merging, and TSV). In particular, on the ViT-L/14 benchmark for 8-task merging, a single merged model reaches 95.1, closely matching the average performance of eight task-specific experts (95.8).

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