Superficial Self-Improved Reasoners Benefit from Model Merging

• URL: http://arxiv.org/abs/2503.02103v1
• Date: Mon, 03 Mar 2025 22:41:25 GMT
• Title: Superficial Self-Improved Reasoners Benefit from Model Merging
• Authors: Xiangchi Yuan, Chunhui Zhang, Zheyuan Liu, Dachuan Shi, Soroush Vosoughi, Wenke Lee,
• Abstract summary: Self-improvement as a solution to synthesizing high-quality data corpus.<n>In particular, our analysis reveals that even when LMs show improved in-domain (ID) reasoning accuracy, they actually compromise their generalized reasoning capabilities.<n>We propose Iterative Model Merging (IMM), a method that strategically combines weights from original and self-improved models to preserve generalization.
• Score: 38.72827436256771
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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