Rethinking Expert Training for Model Merging with Prompt Learning

• URL: http://arxiv.org/abs/2607.24465v1
• Date: Mon, 27 Jul 2026 14:04:48 GMT
• Title: Rethinking Expert Training for Model Merging with Prompt Learning
• Abstract summary: Model merging aims to combine multiple domain-specialized experts trained from a shared foundation model into a single multi-task model.<n>Existing approaches largely focus on improving the merging procedure itself.<n>We introduce Dual-Tuned Experts (DTEs), a two-stage training strategy that first learns prompts and then fine-tunes the vision encoder.
• Score: 29.003417422329715
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model merging aims to combine multiple domain-specialized experts trained from a shared foundation model into a single multi-task model. Existing approaches largely focus on improving the merging procedure itself and typically assume experts obtained through full-parameter fine-tuning. In this work, we revisit expert training for model merging. We first show that prompt-based adaptation provides a strong baseline: independently learned prompts can be exploited across tasks while keeping the backbone fixed, avoiding the interference introduced by weight merging. Building on this observation, we introduce Dual-Tuned Experts (DTEs), a two-stage training strategy that first learns prompts and then fine-tunes the vision encoder. This reduces the magnitude of task-specific parameter updates and produces experts with higher merge compatibility. Experiments across multiple CLIP architectures, full fine-tuning, and LoRA experts show that DTEs consistently improve merged performance of standard merging approaches and remain effective even when combining heterogeneous sets of experts.

Related papers

• Tying the Loop -- Tied Expert Layers in Mixture-of-Experts Language Models [50.34585122347149]
  We introduce Expert Tying, an architectural modification that shares expert parameters across consecutive transformer layers.<n>We evaluate this approach across common, state-of-the-art architectures, including OLMoE, Qwen3, and DeepSeek-style MoEs.
  arXiv  Detail & Related papers  (2026-06-15T15:08:09Z)
• Sparse Subspace-to-Expert Sharing for Task-Agnostic Continual Learning [10.01449025634975]
  Existing methods treat parameters uniformly, failing to distinguish between specific task knowledge and shared capabilities.<n>We introduce Mixture of Sparse Experts for Task Agnostic Continual Learning (SETA), a framework that resolves the plasticity-stability conflict through adaptive sparse subspace decomposition.<n>We show SETA achieves competitive or superior overall performance relative to state-of-the-art continual learning baselines.
  arXiv  Detail & Related papers  (2026-06-05T17:53:52Z)
• PRISM: Synergizing Vision Foundation Models via Self-organized Expert Specialization [25.22639372242625]
  We introduce textbfPRISM, a novel dual-stream Mixture-of-Experts (MoE) framework that synergizes VFMs via modular specialization.<n>Experiments on PASCAL-Context and NYUD-v2 show that textbfPRISM establishes a new state of the art, validating that sparse, emergent specialization is a scalable approach for integrating diverse visual knowledge.
  arXiv  Detail & Related papers  (2026-06-02T10:28:32Z)
• Co-Evolving Policy Distillation [56.500154041818746]
  Co-Evolving Policy Distillation (CoPD) encourages parallel training of experts and introduces OPD during each expert's ongoing RLVR training.<n>Experiments validate that CoPD achieves all-in-one integration of text, image, and video reasoning capabilities.
  arXiv  Detail & Related papers  (2026-04-29T18:24:11Z)
• UniT: Unified Multimodal Chain-of-Thought Test-time Scaling [85.590774707406]
  Unified models can handle both multimodal understanding and generation within a single architecture, yet they typically operate in a single pass without iteratively refining their outputs.<n>We introduce UniT, a framework for multimodal test-time scaling that enables a single unified model to reason, verify, and refine across multiple rounds.
  arXiv  Detail & Related papers  (2026-02-12T18:59:49Z)
• Split-on-Share: Mixture of Sparse Experts for Task-Agnostic Continual Learning [10.01449025634975]
  Continual learning in Large Language Models (LLMs) is hindered by the plasticity-stability dilemma.<n>We introduce SETA, a framework that resolves the plasticity-stability conflict by decomposing the model into modular subspaces.<n>We show that SETA consistently outperforms state-of-the-art parameter-efficient fine-tuning-based continual learning methods.
  arXiv  Detail & Related papers  (2026-01-24T22:39:22Z)
• HBridge: H-Shape Bridging of Heterogeneous Experts for Unified Multimodal Understanding and Generation [72.69742127579508]
  Recent unified models integrate understanding experts (e.g., LLMs) with generative experts (e.g., diffusion models)<n>In this work, we propose HBridge, an asymmetric H-shaped architecture that enables heterogeneous experts to optimally leverage pretrained priors.<n> Extensive experiments across multiple benchmarks demonstrate the effectiveness and superior performance of HBridge.
  arXiv  Detail & Related papers  (2025-11-25T17:23:38Z)
• Adaptive Shared Experts with LoRA-Based Mixture of Experts for Multi-Task Learning [49.90176890917986]
  Mixture-of-Experts (MoE) has emerged as a powerful framework for multi-task learning (MTL)<n>Existing MoE-MTL methods often rely on single-task pretrained backbones and suffer from redundant adaptation and inefficient knowledge sharing.<n>We propose adaptive shared experts (ASE) within a low-rank adaptation (LoRA) based MoE, where shared experts are assigned router-computed gating weights jointly normalized with sparse experts.
  arXiv  Detail & Related papers  (2025-10-01T06:49:19Z)
• Symphony-MoE: Harmonizing Disparate Pre-trained Models into a Coherent Mixture-of-Experts [18.18231276284727]
  Mixture-of-Experts (MoE) models enable scalable performance by activating large parameter sets sparsely.<n>Recent work employs upcycling, reusing a single pre-trained dense model by replicating its feed-forward network (FFN) layers into experts.<n>This paper addresses this limitation by constructing powerful MoE models using experts sourced from multiple identically-architected but disparate pre-trained models.
  arXiv  Detail & Related papers  (2025-09-23T02:07:14Z)
• Unveiling Hidden Collaboration within Mixture-of-Experts in Large Language Models [5.211806751260724]
  We propose a hierarchical sparse dictionary learning (HSDL) method that uncovers the collaboration patterns among experts.<n>We also introduce the Contribution-Aware Expert Pruning (CAEP) algorithm, which effectively prunes low-contribution experts.
  arXiv  Detail & Related papers  (2025-04-16T04:06:15Z)
• Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy [84.11508381847929]
  Sparsely activated Mixture-of-Experts (SMoE) has shown promise to scale up the learning capacity of neural networks. We propose M-SMoE, which leverages routing statistics to guide expert merging. Our MC-SMoE achieves up to 80% memory and a 20% FLOPs reduction, with virtually no loss in performance.
  arXiv  Detail & Related papers  (2023-10-02T16:51:32Z)

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.