![[2602.16189] Beyond Learning: A Training-Free Alternative to Model Adaptation](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.16189] Beyond Learning: A Training-Free Alternative to Model Adaptation
Summary
The paper presents a novel approach to model adaptation in language models, introducing a training-free method that utilizes internal module transplantation to enhance performance without additional training.
Why It Matters
This research addresses the limitations of existing model adaptation techniques, which are often resource-intensive. By proposing a training-free alternative, it opens new avenues for improving language model performance efficiently, which is crucial for real-time applications in AI.
Key Takeaways
- Introduces a training-free method for model adaptation using internal module transplantation.
- Demonstrates significant performance improvements in underperforming language models.
- Establishes empirical evidence for task-localized modularity in language models.
- Proposes a new research area focused on model transplantation.
- Highlights the potential for immediate functional changes without the need for extensive retraining.
Computer Science > Computation and Language arXiv:2602.16189 (cs) [Submitted on 18 Feb 2026] Title:Beyond Learning: A Training-Free Alternative to Model Adaptation Authors:Namkyung Yoon, Kyeonghyun Yoo, Wooyong Jung, Sanghong Kim, Hwangnam Kim View a PDF of the paper titled Beyond Learning: A Training-Free Alternative to Model Adaptation, by Namkyung Yoon and 4 other authors View PDF HTML (experimental) Abstract:Despite the continuous research and evolution of language models, they sometimes underperform previous versions. Existing approaches to overcome these challenges are resource-intensive, highlighting the need for alternatives that enable immediate action. We assume that each language model has a local module inside that is suitable for a specific function. First, this work identifies a set of modules showing consistent and local activation changes under an inference workload through activation-based analysis. Subsequently, we transplant an internal module that is properly activated for a specific task into the target model, leading to immediate and measurable functional changes without additional training or fine-tuning. To experimentally demonstrate the effectiveness of the transplant technique, we quantify the relationship between transplant strength and performance improvement under different conditions for two language models. In the cross-generation setting, we find that transplanting activation-selected modules can substantially improve the underperforming mod...
