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An Empirical Study and Theoretical Explanation on Task-Level Model-Merging Collapse

arXiv:2603.09463v1 Announce Type: new Abstract: Model merging unifies independently fine-tuned LLMs from the same base, enabling reuse and integration of parallel development efforts without retraining. However, in practice we observe that merging does not always succeed: certain combinations of task-specialist models suffer from catastrophic performance degradation after merging. We refer to this failure mode as merging collapse. Intuitively, collapse arises when the learned representations or parameter adjustments for different tasks are fundamentally incompatible, so that merging forces destructive interference rather than synergy. In this paper, we identify and characterize the phenomenon of task-level merging collapse, where certain task combinations consistently trigger huge performance degradation across all merging methods. Through extensive experiments and statistical analysis, we demonstrate that representational incompatibility between tasks is strongly correlated with merg

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Yuan Cao, Dezhi Ran, Yuzhe Guo, Mengzhou Wu, Simin Chen, Linyi Li, Wei Yang, Tao Xie · · 1 min read · 9 views
#cs.AI

arXiv:2603.09463v1 Announce Type: new Abstract: Model merging unifies independently fine-tuned LLMs from the same base, enabling reuse and integration of parallel development efforts without retraining. However, in practice we observe that merging does not always succeed: certain combinations of task-specialist models suffer from catastrophic performance degradation after merging. We refer to this failure mode as merging collapse. Intuitively, collapse arises when the learned representations or parameter adjustments for different tasks are fundamentally incompatible, so that merging forces destructive interference rather than synergy. In this paper, we identify and characterize the phenomenon of task-level merging collapse, where certain task combinations consistently trigger huge performance degradation across all merging methods. Through extensive experiments and statistical analysis, we demonstrate that representational incompatibility between tasks is strongly correlated with merging collapse, while parameter-space conflict metrics show minimal correlation, challenging conventional wisdom in model merging literature. We provide a theoretical explanation on this phenomenon through rate-distortion theory with a dimension-dependent bound, establishing fundamental limits on task mergeability regardless of methodology.

Executive Summary

This empirical study and theoretical explanation on task-level model-merging collapse sheds light on the phenomenon where certain task combinations trigger performance degradation after merging large language models (LLMs). Through extensive experiments and statistical analysis, the authors demonstrate a strong correlation between representational incompatibility and merging collapse. The study challenges conventional wisdom in model merging literature by showing minimal correlation with parameter-space conflict metrics. The authors provide a theoretical explanation using rate-distortion theory with a dimension-dependent bound, establishing fundamental limits on task mergeability. This research has significant implications for the field of natural language processing, particularly in the context of parallel development efforts and model reuse.

Key Points

  • ▸ Task-level model-merging collapse is a phenomenon where certain task combinations trigger performance degradation after merging LLMs
  • ▸ Representational incompatibility is strongly correlated with merging collapse, challenging conventional wisdom
  • ▸ Rate-distortion theory with a dimension-dependent bound provides a theoretical explanation for this phenomenon

Merits

Strength

The authors provide a comprehensive empirical study and theoretical explanation, shedding light on a critical issue in model merging literature

Insightful Findings

The study demonstrates a strong correlation between representational incompatibility and merging collapse, challenging conventional wisdom

Methodological Rigor

The authors employ extensive experiments and statistical analysis to support their findings

Demerits

Limitation

The study focuses on task-level model-merging collapse and does not explore other potential failure modes or applications

Theoretical Complexity

The use of rate-distortion theory with a dimension-dependent bound may be challenging for readers without a strong background in mathematics and information theory

Scope for Generalization

The study's findings may not be generalizable to other domains or applications beyond natural language processing

Expert Commentary

This study is a significant contribution to the field of natural language processing, particularly in the context of model merging. The authors' findings challenge conventional wisdom and provide a new perspective on the limitations of model merging. The use of rate-distortion theory with a dimension-dependent bound is a novel and insightful approach to understanding task-level model-merging collapse. However, the study's limitations, such as the scope for generalization and the theoretical complexity, should be acknowledged. Nevertheless, this research has the potential to significantly impact the development of model merging methods and techniques, and its findings should be carefully considered by researchers and practitioners in the field.

Recommendations

  • ✓ Recommendation 1: Future research should focus on developing new model merging methods that accommodate representational incompatibility and parameter-space conflict metrics
  • ✓ Recommendation 2: Theoretical frameworks, such as rate-distortion theory, should be further explored to provide a deeper understanding of task-level model-merging collapse and its implications

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