RanDeS: Randomized Delta Superposition for Multi-Model Compression

• URL: http://arxiv.org/abs/2505.11204v1
• Date: Fri, 16 May 2025 13:02:12 GMT
• Title: RanDeS: Randomized Delta Superposition for Multi-Model Compression
• Authors: Hangyu Zhou, Aaron Gokaslan, Volodymyr Kuleshov, Bharath Hariharan,
• Abstract summary: We reformulate model merging as a compress-and-retrieve scheme, revealing that the task interference arises from the summation of irrelevant deltas during model retrieval.<n>We show that this approach drastically reduces interference, improving performance across both vision and language tasks.
• Score: 35.84370778415708
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: From a multi-model compression perspective, model merging enables memory-efficient serving of multiple models fine-tuned from the same base, but suffers from degraded performance due to interference among their task-specific parameter adjustments (i.e., deltas). In this paper, we reformulate model merging as a compress-and-retrieve scheme, revealing that the task interference arises from the summation of irrelevant deltas during model retrieval. To address this issue, we use random orthogonal transformations to decorrelate these vectors into self-cancellation. We show that this approach drastically reduces interference, improving performance across both vision and language tasks. Since these transformations are fully defined by random seeds, adding new models requires no extra memory. Further, their data- and model-agnostic nature enables easy addition or removal of models with minimal compute overhead, supporting efficient and flexible multi-model serving.

Related papers

• Neural Parameter Search for Slimmer Fine-Tuned Models and Better Transfer [17.463052541838504]
  Fine-tuned models often struggle outside their specific domains and exhibit considerable redundancy.<n>Recent studies suggest that combining a pruned fine-tuned model with the original pre-trained model can mitigate interference when merging model parameters across tasks.<n>We introduce a novel method called Neural Pruning (NPS-Pruning) for slimming down fine-tuned models.
  arXiv  Detail & Related papers  (2025-05-24T14:27:20Z)
• Dynamic Base model Shift for Delta Compression [53.505380509713575]
  Delta compression attempts to lower the costs by reducing the redundancy of delta parameters.<n>Existing methods by default employ the pretrained model as the base model and compress the delta parameters for every task.<n>We propose Dynamic Base Model Shift (DBMS), which dynamically adapts the base model to the target task before performing delta compression.
  arXiv  Detail & Related papers  (2025-05-16T15:11:19Z)
• ImPart: Importance-Aware Delta-Sparsification for Improved Model Compression and Merging in LLMs [9.435738597849447]
  ImPart is a novel importance-aware delta sparsification approach.<n>It adjusts sparsity ratios of different singular vectors based on their importance.
  arXiv  Detail & Related papers  (2025-04-17T16:39:36Z)
• Merging Models on the Fly Without Retraining: A Sequential Approach to Scalable Continual Model Merging [75.93960998357812]
  Deep model merging represents an emerging research direction that combines multiple fine-tuned models to harness their capabilities across different tasks and domains.<n>Current model merging techniques focus on merging all available models simultaneously, with weight matrices-based methods being the predominant approaches.<n>We propose a training-free projection-based continual merging method that processes models sequentially.
  arXiv  Detail & Related papers  (2025-01-16T13:17:24Z)
• Merging Feed-Forward Sublayers for Compressed Transformers [16.746335565636976]
  We present a novel approach to model compression by merging similar parameter groups within a model.<n>Specifically, we select, align, and merge separate feed-forward sublayers in Transformer models.<n>We demonstrate performance comparable to the original models while combining more than a third of model feed-forward sublayers.
  arXiv  Detail & Related papers  (2025-01-10T17:25:11Z)
• Modeling Multi-Task Model Merging as Adaptive Projective Gradient Descent [72.10987117380584]
  Merging multiple expert models offers a promising approach for performing multi-task learning without accessing their original data.<n>We find existing methods discard task-specific information that, while causing conflicts, is crucial for performance.<n>Our approach consistently outperforms previous methods, achieving state-of-the-art results across diverse architectures and tasks in both vision and NLP domains.
  arXiv  Detail & Related papers  (2025-01-02T12:45:21Z)
• 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)
• Representation Surgery for Multi-Task Model Merging [57.63643005215592]
  Multi-task learning (MTL) compresses the information from multiple tasks into a unified backbone to improve computational efficiency and generalization. Recent work directly merges multiple independently trained models to perform MTL instead of collecting their raw data for joint training. By visualizing the representation distribution of existing model merging schemes, we find that the merged model often suffers from the dilemma of representation bias.
  arXiv  Detail & Related papers  (2024-02-05T03:39:39Z)
• Sparse Model Soups: A Recipe for Improved Pruning via Model Averaging [24.64264715041198]
  We introduce Sparse Model Soups (SMS), a novel method for merging sparse models by initiating each prune-retrain cycle with the averaged model from the previous phase. SMS preserves sparsity, exploits sparse network benefits, is modular and fully parallelizable, and substantially improves IMP's performance.
  arXiv  Detail & Related papers  (2023-06-29T08:49:41Z)
• Understanding Parameter Sharing in Transformers [53.75988363281843]
  Previous work on Transformers has focused on sharing parameters in different layers, which can improve the performance of models with limited parameters by increasing model depth. We show that the success of this approach can be largely attributed to better convergence, with only a small part due to the increased model complexity. Experiments on 8 machine translation tasks show that our model achieves competitive performance with only half the model complexity of parameter sharing models.
  arXiv  Detail & Related papers  (2023-06-15T10:48:59Z)

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.