ReasonAny: Incorporating Reasoning Capability to Any Model via Simple and Effective Model Merging

• URL: http://arxiv.org/abs/2601.05560v1
• Date: Fri, 09 Jan 2026 06:19:00 GMT
• Title: ReasonAny: Incorporating Reasoning Capability to Any Model via Simple and Effective Model Merging
• Abstract summary: We propose a novel merging framework that resolves the reasoning-domain performance collapse through Contrastive Gradient Identification.<n>Experiments across safety, biomedicine, and finance domains show that ReasonAny effectively synthesizes "Reasoning + X" capabilities.
• Score: 46.06799235021118
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Reasoning Models (LRMs) with long chain-of-thought reasoning have recently achieved remarkable success. Yet, equipping domain-specialized models with such reasoning capabilities, referred to as "Reasoning + X", remains a significant challenge. While model merging offers a promising training-free solution, existing methods often suffer from a destructive performance collapse: existing methods tend to both weaken reasoning depth and compromise domain-specific utility. Interestingly, we identify a counter-intuitive phenomenon underlying this failure: reasoning ability predominantly resides in parameter regions with low gradient sensitivity, contrary to the common assumption that domain capabilities correspond to high-magnitude parameters. Motivated by this insight, we propose ReasonAny, a novel merging framework that resolves the reasoning-domain performance collapse through Contrastive Gradient Identification. Experiments across safety, biomedicine, and finance domains show that ReasonAny effectively synthesizes "Reasoning + X" capabilities, significantly outperforming state-of-the-art baselines while retaining robust reasoning performance.

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