Superficial Self-Improved Reasoners Benefit from Model Merging
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912671284068352 |
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| author | Yuan, Xiangchi Zhang, Chunhui Liu, Zheyuan Shi, Dachuan Pan, Leyan Vosoughi, Soroush Lee, Wenke |
| author_facet | Yuan, Xiangchi Zhang, Chunhui Liu, Zheyuan Shi, Dachuan Pan, Leyan Vosoughi, Soroush Lee, Wenke |
| contents | As scaled language models (LMs) approach human-level reasoning capabilities, self-improvement emerges as a solution to synthesizing high-quality data corpus. While previous research has identified model collapse as a risk in self-improvement, where model outputs become increasingly deterministic, we discover a more fundamental challenge: the superficial self-improved reasoners phenomenon. In particular, our analysis reveals that even when LMs show improved in-domain (ID) reasoning accuracy, they actually compromise their generalized reasoning capabilities on out-of-domain (OOD) tasks due to memorization rather than genuine. Through a systematic investigation of LM architecture, we discover that during self-improvement, LM weight updates are concentrated in less reasoning-critical layers, leading to superficial learning. To address this, we propose Iterative Model Merging (IMM), a method that strategically combines weights from original and self-improved models to preserve generalization while incorporating genuine reasoning improvements. Our approach effectively mitigates both LM collapse and superficial learning, moving towards more stable self-improving systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02103 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Superficial Self-Improved Reasoners Benefit from Model Merging Yuan, Xiangchi Zhang, Chunhui Liu, Zheyuan Shi, Dachuan Pan, Leyan Vosoughi, Soroush Lee, Wenke Computation and Language As scaled language models (LMs) approach human-level reasoning capabilities, self-improvement emerges as a solution to synthesizing high-quality data corpus. While previous research has identified model collapse as a risk in self-improvement, where model outputs become increasingly deterministic, we discover a more fundamental challenge: the superficial self-improved reasoners phenomenon. In particular, our analysis reveals that even when LMs show improved in-domain (ID) reasoning accuracy, they actually compromise their generalized reasoning capabilities on out-of-domain (OOD) tasks due to memorization rather than genuine. Through a systematic investigation of LM architecture, we discover that during self-improvement, LM weight updates are concentrated in less reasoning-critical layers, leading to superficial learning. To address this, we propose Iterative Model Merging (IMM), a method that strategically combines weights from original and self-improved models to preserve generalization while incorporating genuine reasoning improvements. Our approach effectively mitigates both LM collapse and superficial learning, moving towards more stable self-improving systems. |
| title | Superficial Self-Improved Reasoners Benefit from Model Merging |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.02103 |