M-Loss: Quantifying Model Merging Compatibility with Limited Unlabeled Data

• URL: http://arxiv.org/abs/2602.08564v1
• Date: Mon, 09 Feb 2026 12:03:36 GMT
• Title: M-Loss: Quantifying Model Merging Compatibility with Limited Unlabeled Data
• Authors: Tiantong Wang, Yiyang Duan, Haoyu Chen, Tiantong Wu, Wei Yang Bryan Lim,
• Abstract summary: We introduce Merging-ensembling loss (M-Loss), a novel evaluation metric.<n>M-Loss quantifies the compatibility of merging source models using very limited unlabeled data.<n>Our theoretical analysis and empirical evaluations demonstrate that incorporating M-Loss into the merging process significantly improves the alignment between merged models and model ensembling.
• Score: 9.502531621979694
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training of large-scale models is both computationally intensive and often constrained by the availability of labeled data. Model merging offers a compelling alternative by directly integrating the weights of multiple source models without requiring additional data or extensive training. However, conventional model merging techniques, such as parameter averaging, often suffer from the unintended combination of non-generalizable features, especially when source models exhibit significant weight disparities. Comparatively, model ensembling generally provides more stable and superior performance that aggregates multiple models by averaging outputs. However, it incurs higher inference costs and increased storage requirements. While previous studies experimentally showed the similarities between model merging and ensembling, theoretical evidence and evaluation metrics remain lacking. To address this gap, we introduce Merging-ensembling loss (M-Loss), a novel evaluation metric that quantifies the compatibility of merging source models using very limited unlabeled data. By measuring the discrepancy between parameter averaging and model ensembling at layer and node levels, M-Loss facilitates more effective merging strategies. Specifically, M-Loss serves both as a quantitative criterion of the theoretical feasibility of model merging, and a guide for parameter significance in model pruning. Our theoretical analysis and empirical evaluations demonstrate that incorporating M-Loss into the merging process significantly improves the alignment between merged models and model ensembling, providing a scalable and efficient framework for accurate model consolidation.

Related papers

• Mixture-of-Experts Models in Vision: Routing, Optimization, and Generalization [0.0]
  We study MoE behavior in an image classification setting, focusing on predictive performance, expert utilization, and generalization.<n>We compare dense, SoftMoE, and SparseMoE classifier heads on the CIFAR10 dataset under comparable model capacity.<n>Both MoE variants achieve slightly higher validation accuracy than the dense baseline while maintaining balanced expert utilization through regularization.<n>We find that SoftMoE exhibits higher sharpness by these metrics, while Dense and SparseMoE lie in a similar curvature regime, despite all models achieving comparable generalization performance.
  arXiv  Detail & Related papers  (2026-01-21T14:22:25Z)
• The Law of Multi-Model Collaboration: Scaling Limits of Model Ensembling for Large Language Models [54.51795784459866]
  We propose a theoretical framework of performance scaling for multi-model collaboration.<n>We show that multi-model systems follow a power-law scaling with respect to the total parameter count.<n> ensembles of heterogeneous model families achieve better performance scaling than those formed within a single model family.
  arXiv  Detail & Related papers  (2025-12-29T09:55:12Z)
• Model Merging via Multi-Teacher Knowledge Distillation [11.543771846135021]
  We introduce a novel flatness-aware PAC-Bayes generalization bound specifically for the model merging setting.<n>We frame model merging as multi-teacher knowledge distillation on scarce, unlabeled data.<n>We formally demonstrate that minimizing the student-teacher Kullback-Leibler divergence directly tightens the upper bound on the merged model's excess risk.
  arXiv  Detail & Related papers  (2025-12-24T17:10:44Z)
• Towards Reversible Model Merging For Low-rank Weights [5.100622189286672]
  Model merging aims to combine multiple fine-tuned models into a single set of weights that performs well across all source tasks.<n>We show that applying conventional merging methods to low-rank weights leads to severe performance degradation in the merged model.<n>We propose a fundamentally different approach: instead of collapsing all adapters into one set of weights, we construct a compact basis.<n>This reframes merging as generating a reconstruction-capable model space rather than producing a single merged model.
  arXiv  Detail & Related papers  (2025-10-15T23:22:38Z)
• Why Do More Experts Fail? A Theoretical Analysis of Model Merging [51.18155031364046]
  Model merging dramatically reduces storage and computational resources by combining multiple expert models into a single multi-task model.<n>Recent model merging methods have shown promising results, but struggle to maintain performance gains as the number of merged models increases.<n>We show that the limited effective parameter space imposes a strict constraint on the number of models that can be successfully merged.
  arXiv  Detail & Related papers  (2025-05-27T14:10:46Z)
• Multi-Level Collaboration in Model Merging [56.31088116526825]
  This paper explores the intrinsic connections between model merging and model ensembling.<n>We find that even when previous restrictions are not met, there is still a way for model merging to attain a near-identical and superior performance similar to that of ensembling.
  arXiv  Detail & Related papers  (2025-03-03T07:45:04Z)
• Investigating the Impact of Model Complexity in Large Language Models [3.7919508292745676]
  Large Language Models (LLMs) based on the pre-trained fine-tuning paradigm have become pivotal in solving natural language processing tasks. In this paper, we focus on autoregressive LLMs and propose to employ Hidden Markov Models (HMMs) to model them.
  arXiv  Detail & Related papers  (2024-10-01T13:53:44Z)
• Minimal Variance Model Aggregation: A principled, non-intrusive, and versatile integration of black box models [0.2455468619225742]
  We introduce Minimal Empirical Variance Aggregation (MEVA), a data-driven framework that integrates predictions from various models.<n>This non-intrusive, model-agnostic approach treats the contributing models as black boxes and accommodates outputs from diverse methodologies.
  arXiv  Detail & Related papers  (2024-09-25T18:33:21Z)
• EMR-Merging: Tuning-Free High-Performance Model Merging [55.03509900949149]
  We show that Elect, Mask & Rescale-Merging (EMR-Merging) shows outstanding performance compared to existing merging methods. EMR-Merging is tuning-free, thus requiring no data availability or any additional training while showing impressive performance.
  arXiv  Detail & Related papers  (2024-05-23T05:25:45Z)
• Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling [54.94763543386523]
  Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the ( aggregate) posterior to encourage statistical independence of the latent factors. We present a novel multi-stage modeling approach where the disentangled factors are first learned using a penalty-based disentangled representation learning method. Then, the low-quality reconstruction is improved with another deep generative model that is trained to model the missing correlated latent variables.
  arXiv  Detail & Related papers  (2020-10-25T18:51:15Z)
• Robust Finite Mixture Regression for Heterogeneous Targets [70.19798470463378]
  We propose an FMR model that finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously. We provide non-asymptotic oracle performance bounds for our model under a high-dimensional learning framework. The results show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-10-12T03:27:07Z)
• Non-asymptotic oracle inequalities for the Lasso in high-dimensional mixture of experts [2.794896499906838]
  We consider the class of softmax-gated Gaussian MoE (SGMoE) models with softmax gating functions and Gaussian experts. To the best of our knowledge, we are the first to investigate the $l_1$-regularization properties of SGMoE models from a non-asymptotic perspective. We provide a lower bound on the regularization parameter of the Lasso penalty that ensures non-asymptotic theoretical control of the Kullback--Leibler loss of the Lasso estimator for SGMoE models.
  arXiv  Detail & Related papers  (2020-09-22T15:23:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.