Title: Can MLLMs Absorb Math Reasoning Abilities from LLMs as Free Lunch?
Venue: NeurIPS 2025
Accepted: yes
Keywords: Multi-modal reasoning

math reasoning

model merging
Abstract: Math reasoning has been one crucial ability of large language models (LLMs), where significant advancements have been achieved in recent years. However, most efforts focus on LLMs by curating high-quality annotation data and intricate training (or inference) paradigms, while the math reasoning performance of multi-modal LLMs (MLLMs) remains lagging behind. Since the MLLM typically consists of an LLM and vision block, we wonder: \textit{Can MLLMs directly absorb math reasoning abilities from off-the-shelf math LLMs without tuning?} Recent model-merging approaches may offer insights into this question. However, they overlook the alignment between the MLLM and LLM, where we find that there is a large gap between their parameter spaces, resulting in lower performance. 
Our empirical evidence reveals two key factors behind this issue: the identification of crucial reasoning-associated layers in the model and the mitigation of the gaps in parameter space. Based on the empirical insights, we propose \textbf{IP-Merging} that first \textbf{I}dentifies the reasoning-associated parameters in both MLLM and Math LLM, then \textbf{P}rojects them into the subspace of MLLM aiming to maintain the alignment, finally merges parameters in this subspace. IP-Merging is a tuning-free approach since parameters are directly adjusted. Extensive experiments demonstrate that our IP-Merging method can enhance the math reasoning ability of MLLMs directly from Math LLMs without compromising their other capabilities.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes IP-Merging, a tuning-free model merging technique to enhance the mathematical reasoning capabilities of MLLMs by merging them with math-specialized LLMs. It first identifies math-reasoning-associated parameters, rescales and projects them into a shared subspace, and merges the models. Compared with other model merging baselines, IP-merging is shown to more effectively improve math reasoning performance on MathVerse and MathVista without degrading general capabilities on MMMU.\n\nSTRENGTHS AND WEAKNESSES:\n### **Strengths**\n\n1. The paper provides a rigorous empirical analysis of why conventional merging methods are ineffective, convincingly demonstrating the \"parameter space gap.\" This analysis offers a strong motivation for the proposed work.\n2. The proposed IP-Merging method is novel and effectively addresses the diagnosed issues. Its training-free nature makes it a practical solution for enhancing model capabilities.\n3. Experimental results effectively demonstrate that IP-Merging enhances the mathematical reasoning of MLLMs without compromising performance on MMMU, and IP-Merging is shown more competitive performance over baselines.\n4. Overall, the paper is well-written, clear, and easy to follow. (Though I still have some suggestions for improvement, please see below.)\n\n### **Weaknesses**\n\n1. As acknowledged in the limitations section, the method currently only supports merging models of the same size and base architecture. This indeed significantly limits its applicability and potential adoption.\n\n2. The \"free lunch\" claim is not sufficiently supported—evaluating only on MMMU is not enough. A broader set of evaluations is needed to verify whether the merged models truly retain their original capabilities.\n\n3. Beyond performance metrics, an analysis of error or failure cases before and after merging could offer additional insights and perspectives.\n\n4. Ablation studies on the hyperparameters $\\alpha_1$ and $\\alpha_2$ are missing.\n\n\n\n### **Writing & Presentation (Minor)**\n\n1. The experiments involving the merging of multiple models are valuable. I recommend moving them from the appendix into the main text and providing a more in-depth analysis.\n2. The readability of Table 9 is poor. Consider replacing it with line charts (e.g., a matrix of subfigures), which could illustrate trends more effectively.\n\nQUESTIONS:\nPlease refer to the weaknesses section first.\n\n1. Can this method be applied to domains beyond math? It seems such a merging strategy could be generalizable to other domains—could you elaborate? If so, why only focus on math reasoning in this paper?\n\n2. Could you provide qualitative examples where the merged MLLM successfully solves a problem that the base model fails to address? This would offer intuition and insight about the improvements.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a method for improving the math reasoning capabilities of multimodal large language models (MLLMs) by directly merging parameters from math-focused LLMs, without additional training or data. The proposed IP-Merging approach identifies reasoning-related layers in both models and projects selected parameters from the math LLM into the MLLM’s subspace to address parameter misalignment. Experimental results on benchmarks such as MathVista and MathVerse, using models like LLaVA and Qwen2-VL, show that IP-Merging enhances math reasoning performance of MLLMs while maintaining their general abilities.\n\nSTRENGTHS AND WEAKNESSES:\n***Strengths***\n1. The paper introduces IP-Merging, a new parameter merging method that enables MLLMs to directly acquire math reasoning abilities from math-specialized LLMs without any additional training or data.\n2. The approach incorporates subspace similarity-based parameter selection and projection, effectively identifying and aligning reasoning-associated parameters between models to facilitate knowledge transfer.\n3. Experimental results demonstrate that IP-Merging consistently improves the math reasoning performance of MLLMs on benchmarks such as MathVista and MathVerse, while maintaining the general capabilities of the models.\n\n***Weaknesses***\n1. **Lack of evaluation on recent reasoning models:** Although the paper reports results on G-LLaVA and similar math-specific models, these are based on older architectures (2023). For example, after applying IP-Merging, G-LLaVA only achieves a MathVista score of 29.6, whereas recent 7B reasoning models can reach nearly 70 on the same benchmark. The lack of evaluation on recent reasoning models makes it difficult to judge whether the effectiveness of IP-Merging generalizes to current high-performing models.\n2. **Lack of diverse and strong general baselines:** The experiments only consider LLaVA-1.5-7B and Qwen-2-VL-7B as general MLLMs. The absence of more recent, larger, or more competitive models, as well as different training strategies and sizes, limits the assessment of IP-Merging’s robustness and general applicability.\n3. **Insufficient benchmark coverage:** The reasoning benchmarks used in the experiments are limited to MathVista and MathVerse. Results on additional benchmarks such as MathVision, WeMath and Dynamath would provide a more comprehensive assessment. In particular, WeMath and Dynamath can highlight reasoning mechanism deficiencies, while MathVision focuses on more challenging problems.\n4. **Experimental results and reporting require clarification:** There are inconsistencies in reported numbers, such as a MathVista score of 55.4 in the paper compared to the official 58.2. Additionally, for MMMU (Table 3), the baseline (Qwen-2-VL-7B) and IP-Merging results (including sub-categories) are exactly the same. These points require clarification, as they directly impact the credibility of the experimental conclusions.\n5. **Architectural restriction:** IP-Merging requires both the MLLM and the math LLM to have the same architecture and parameter size, significantly limiting its applicability in practical scenarios involving heterogeneous or larger-scale models.\n\nQUESTIONS:\n**See W1&W2&W3&W4.** (The current evaluation mainly focuses on relatively small and outdated models. Can the authors provide results on more recent and larger MLLMs, especially those with stronger baseline performance? As plug-and-play methods often become less effective as baselines improve, it would be important to discuss whether IP-Merging still offers meaningful gains in these settings. If the improvements are marginal or absent with recent models, what are the main factors limiting its effectiveness?)\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

comments: SUMMARY:
This article attempts to address the issue of merging parameters between Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) to integrate the reasoning capabilities of an LLM focused on mathematical reasoning into a general MLLM, thereby enhancing the MLLM's ability to handle visual math-related problems. The authors identify two key challenges: how to select appropriate parameters and how to better align them. In response, the authors propose the IP-Merging method, which requires no additional tuning. Moreover, this method not only achieves promising results but also maintains the original capabilities of the MLLM across other tasks.

STRENGTHS AND WEAKNESSES:
Strengths: The writing is fluent, and the analysis of the adjustments to the problem is insightful. The proposed method achieves excellent results without affecting the inherent capabilities of the MLLM.

Weaknesses:

1.Can this method be extended to models other than Math LLMs, such as code LLMs?

2.There are many methods that have found training the visual encoder together can yield good results (e.g., Cambrian-1). Has the author considered this scenario where the visual encoder has been trained?

3.You can consider the results of the benchmark mentioned in MathGlance (https://arxiv.org/pdf/2503.20745) to provide a more insightful assessment.

4.There is some misalignment of text in Figure 2.

QUESTIONS:
Please see weaknesses.

LIMITATIONS:
N/A
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the challenge of enhancing the mathematical reasoning abilities of multi-modal large language models (MLLMs) by leveraging the expertise of specialized, text-only math LLMs. The authors explore whether this transfer of knowledge can occur without any expensive fine-tuning, proposing that a significant gap in the parameter spaces of these models is the primary obstacle to direct merging.\n\nTo overcome this, the paper introduces a tuning-free method named IP-Merging. This approach first identifies the most relevant parameters for the task through subspace similarity analysis and then projects them from the Math LLM into the MLLM's subspace to ensure better alignment. The authors validate their method on several visual math reasoning benchmarks, showing that it improves performance on the target task while successfully preserving the model's general capabilities.\n\nSTRENGTHS AND WEAKNESSES:\nThe work's main strength is its focus on a highly relevant and practical problem: finding low-cost ways to enhance powerful models with specialized skills. The diagnostic analysis presented is insightful, providing a clear and data-driven motivation for why simple model merging fails and why a more sophisticated approach is necessary. The experimental validation is also quite solid, especially the inclusion of a general-purpose benchmark to confirm that the method does not cause catastrophic forgetting, which is a critical aspect for any model merging technique.\n\nHowever, the paper has some notable weaknesses that temper its overall impact. The most significant limitation is that the proposed method is currently restricted to models that share an identical architecture and size. This severely narrows its practical applicability, as many real-world use cases would involve transferring knowledge between models of different scales or families. Furthermore, the methodological contribution feels somewhat incremental, as it primarily combines well-established techniques like SVD and task vector arithmetic in a novel application, rather than introducing fundamentally new concepts.\n\nBeyond these broader concerns, some specific experimental results raise further questions. According to Table 2, the merging of LLaVA-1.5-7B shows a very large performance gap (18.8 points) on the GPS subset of MathVista when compared to Ties Merging. This result casts doubt on the effectiveness of IP-Merging for geometric problems. The authors' explanation that this is because GPS requires multiple steps of geometry reasoning is insufficient; if the MLLM truly absorbed the Math LLM's reasoning ability, it should show improvement in this domain. Additionally, the method has not been validated on stronger MLLMs like Qwen-2.5-VL or InternVL 3. For example, InternVL 3 already achieves high performance of 39.8 on MathVerse and 71.6 on MathVista. Verifying that IP-Merging still provides a meaningful benefit to these highly capable models would be very helpful for testing the method's validity.\n\nQUESTIONS:\n1. This work focuses exclusively on mathematical reasoning. Have you considered whether IP-Merging is equally effective for transferring other specialized, text-based abilities, such as code generation, legal knowledge, or medical question-answering? Does the success of the method depend on any particular properties of the task being transferred?\n\n\n2. The similarity threshold `Sω` is a key hyperparameter. Instead of a fixed threshold, have you considered a more adaptive selection strategy? For instance, one could select a certain top percentage of layers based on their similarity score distribution, which might make the method more robust across different pairs of models.\n\n3. The performance on the GPS subset is notably poor compared to Ties Merging, and your explanation for this is quite general. Could you provide a more detailed error analysis for this task? For example, does the model fail more on visual perception of shapes or on applying geometric reasoning? This would help clarify the root cause of this limitation.\n\n4. To better demonstrate the method's utility, it would be valuable to see experiments on more recent, powerful MLLMs like Qwen2.5-VL and InternVL3. Since these models already have strong baseline reasoning abilities, showing that IP-Merging can still provide a meaningful boost would be a critical test of its practical value.\n\n5. The claim that IP-Merging preserves general abilities is supported only by the MMMU benchmark. To make this claim more robust, could you provide evaluation results on other general multi-modal benchmarks, such as MMBench or TextVQA?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}