Merge and Guide: Unifying Model Merging and Guided Decoding for Controllable Multi-Objective Generation

• URL: http://arxiv.org/abs/2510.03782v2
• Date: Thu, 16 Oct 2025 09:11:22 GMT
• Title: Merge and Guide: Unifying Model Merging and Guided Decoding for Controllable Multi-Objective Generation
• Authors: Guofu Xie, Chen Zhang, Xiao Zhang, Yunsheng Shi, Ting Yao, Jun Xu,
• Abstract summary: We introduce Merge-And-GuidE, a two-stage framework that leverages model merging for guided decoding.<n>In Stage 1, MAGE resolves a compatibility problem between the guidance and base models.<n>In Stage 2, we merge explicit and implicit value models into a unified guidance proxy, which then steers the decoding of the base model from Stage 1.
• Score: 49.98025799046136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adapting to diverse user needs at test time is a key challenge in controllable multi-objective generation. Existing methods are insufficient: merging-based approaches provide indirect, suboptimal control at the parameter level, often disregarding the impacts of multiple objectives. While decoding-based guidance is more direct, it typically requires aggregating logits from multiple expert models, incurring significant space overhead and relying heavily on individual model capacity. To address these issues, we introduce Merge-And-GuidE (MAGE), a two-stage framework that leverages model merging for guided decoding. We first identify a critical compatibility problem between the guidance and base models. In Stage 1, MAGE resolves this by dynamically constructing a more robust base model, merging a series of backbone models that account for multiple objectives. In Stage 2, we merge explicit and implicit value models into a unified guidance proxy, which then steers the decoding of the base model from Stage 1. Our analysis empirically validates Linear Mode Connectivity (LMC) in value models, explores the relationship between model merging and prediction ensembling, and demonstrates the enhanced controllability afforded by our approach. Extensive experiments show that our method outperforms existing approaches, achieving superior controllability, Pareto-optimal performance, and enhanced adaptability.

Related papers

• Model Merging in the Essential Subspace [78.5390284258307]
  Model merging aims to integrate multiple task-specific fine-tuned models into a single multi-task model without additional training.<n>Despite extensive research, task interference remains a major obstacle that often undermines the performance of merged models.<n>We propose ESM (Essential Subspace Merging), a robust framework for effective model merging.
  arXiv  Detail & Related papers  (2026-02-23T00:33:38Z)
• Fine-Grained Model Merging via Modular Expert Recombination [33.253051407398836]
  We propose MERGE, a method that enables component-wise model merging and input-aware, on-demand module recombination at inference.<n> MERGE formulates component-wise merging as a bi-objective optimization problem that balances cross-task performance and storage efficiency.<n>We show that MERGE consistently outperforms strong baselines and generalizes effectively.
  arXiv  Detail & Related papers  (2026-02-06T09:55:56Z)
• An Integrated Fusion Framework for Ensemble Learning Leveraging Gradient Boosting and Fuzzy Rule-Based Models [59.13182819190547]
  Fuzzy rule-based models excel in interpretability and have seen widespread application across diverse fields.<n>They face challenges such as complex design specifications and scalability issues with large datasets.<n>This paper proposes an Integrated Fusion Framework that merges the strengths of both paradigms to enhance model performance and interpretability.
  arXiv  Detail & Related papers  (2025-11-11T10:28:23Z)
• Black-box Model Merging for Language-Model-as-a-Service with Massive Model Repositories [21.899117703417517]
  We propose a derivative-free optimization framework based on the evolutionary algorithm (Evo-Merging)<n>Our method consists of two key components: (1) sparsity-based denoising, designed to identify and filter out irrelevant or redundant information across models, and (2) sign-aware scaling, which dynamically computes optimal combination weights for the relevant models based on their performance.<n>Our approach achieves state-of-the-art results on a range of tasks, significantly outperforming existing strong baselines.
  arXiv  Detail & Related papers  (2025-09-16T10:55:50Z)
• OneCAT: Decoder-Only Auto-Regressive Model for Unified Understanding and Generation [91.45421429922506]
  OneCAT is a unified multimodal model that seamlessly integrates understanding, generation, and editing.<n>Our framework eliminates the need for external components such as Vision Transformers (ViT) or vision tokenizer during inference.
  arXiv  Detail & Related papers  (2025-09-03T17:29:50Z)
• SE-Merging: A Self-Enhanced Approach for Dynamic Model Merging [60.83635006372403]
  textttSE-Merging is a self-enhanced model merging framework.<n>We show that textttSE-Merging achieves dynamic model merging without additional training.
  arXiv  Detail & Related papers  (2025-06-22T18:38:41Z)
• Towards Minimizing Feature Drift in Model Merging: Layer-wise Task Vector Fusion for Adaptive Knowledge Integration [14.503741632243646]
  Multi-task model merging aims to consolidate knowledge from multiple task-specific experts into a unified model.<n>Existing methods approach this by minimizing differences between task-specific experts and the unified model.<n>We propose Layer-wise Optimal Task Vector Merging, a technique that explicitly minimizes feature drift between task-specific experts and the unified model.
  arXiv  Detail & Related papers  (2025-05-29T08:11:31Z)
• Dynamic Fisher-weighted Model Merging via Bayesian Optimization [37.02810891820468]
  Existing merging approaches typically involve scaling the parameters model-wise or integrating parameter importance parameter-wise.<n>We unify these strategies into a more general merging framework, and introduce Dynamic Fisher-weighted Merging (DF-Merge)<n>We show that DF-Merge outperforms strong baselines across models of different sizes and a variety of tasks.
  arXiv  Detail & Related papers  (2025-04-26T18:31:14Z)
• AdaMMS: Model Merging for Heterogeneous Multimodal Large Language Models with Unsupervised Coefficient Optimization [86.8133939108057]
  We propose AdaMMS, a novel model merging method tailored for heterogeneous MLLMs.<n>Our method tackles the challenges in three steps: mapping, merging and searching.<n>As the first model merging method capable of merging heterogeneous MLLMs without labeled data, AdaMMS outperforms previous model merging methods on various vision-language benchmarks.
  arXiv  Detail & Related papers  (2025-03-31T05:13:02Z)
• Fine, I'll Merge It Myself: A Multi-Fidelity Framework for Automated Model Merging [30.38047100067552]
  Reasoning capabilities represent a critical frontier for large language models.<n>One way to efficiently supplement capabilities with is by model merging.<n>We propose an Automated Model Merging Framework that enables fine-grained exploration of merging strategies.
  arXiv  Detail & Related papers  (2025-02-06T12:47:25Z)
• Parameter Competition Balancing for Model Merging [13.66727853299506]
  PCB-Merging is a training-free technique that adjusts the coefficients of each parameter for effective model merging. PCB-Merging achieves substantial performance enhancements across multiple modalities, domains, model sizes, number of tasks, fine-tuning forms, and large language models.
  arXiv  Detail & Related papers  (2024-10-03T11:17:58Z)
• An Empirical Study of Multimodal Model Merging [148.48412442848795]
  Model merging is a technique that fuses multiple models trained on different tasks to generate a multi-task solution. We conduct our study for a novel goal where we can merge vision, language, and cross-modal transformers of a modality-specific architecture. We propose two metrics that assess the distance between weights to be merged and can serve as an indicator of the merging outcomes.
  arXiv  Detail & Related papers  (2023-04-28T15:43:21Z)

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.