Title: Activation-Guided Consensus Merging for Large Language Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: LLM Reasoning

Model Merging

Long-to-Short
Abstract: Recent research has increasingly focused on reconciling the reasoning capabilities of System 2 with the efficiency of System 1. While existing training-based and prompt-based approaches face significant challenges in terms of efficiency and stability, model merging emerges as a promising strategy to integrate the diverse capabilities of different Large Language Models (LLMs) into a unified model. However, conventional model merging methods often assume uniform importance across layers, overlooking the functional heterogeneity inherent in neural components. To address this limitation, we propose \textbf{A}ctivation-Guided \textbf{C}onsensus \textbf{M}erging (\textbf{ACM}), a plug-and-play merging framework that determines layer-specific merging coefficients based on mutual information between activations of pre-trained and fine-tuned models. ACM effectively preserves task-specific capabilities without requiring gradient computations or additional training. Extensive experiments on Long-to-Short (L2S) and general merging tasks demonstrate that ACM consistently outperforms all baseline methods. For instance, in the case of Qwen-7B models, TIES-Merging equipped with ACM achieves a \textbf{55.3\%} reduction in response length while simultaneously improving reasoning accuracy by \textbf{1.3} points. We submit the code with the paper for reproducibility, and it will be publicly available.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes Activation-Guided Consensus Merging (ACM), a novel method for merging Large Language Models (LLMs) by dynamically weighting layer contributions based on mutual information (MI) between activations of pre-trained and fine-tuned models. Unlike traditional merging techniques that assume uniform layer importance, ACM identifies task-specific critical layers, reducing redundancy while preserving performance. Experiments on Long-to-Short (L2S) reasoning and general merging tasks demonstrate that ACM improves reasoning accuracy (e.g., +1.3 points on Qwen-7B) while significantly shortening responses (e.g., -55.3% length). The method is plug-and-play, requiring no additional training or gradients, and outperforms baselines like TIES-Merging and DARE.\n\nSTRENGTHS AND WEAKNESSES:\n1. Evaluated primarily on Qwen and LLaMA families; unclear how ACM performs on architectures with significantly different layer structures (e.g., MoE models).\n\n2. Relies on a curated dataset (s1K) for activation alignment. Performance may degrade with poorly representative or biased calibration data.\n\n3. How does the system 1/2 discussion related to this work \"Visual agents as fast and slow thinkers\" ICLR? Both work has similar motivation.\n\nQUESTIONS:\n1. Why use mutual information instead of other similarity metrics? The paper argues MI captures redundancy, but alternatives (e.g., cosine similarity, KL divergence) might offer different trade-offs in efficiency or interpretability.\n\n2. How does ACM handle conflicting tasks in multi-task merging? The experiments focus on L2S and single-task merging. Can ACM resolve conflicts when merging models fine-tuned for opposing objectives (e.g., conciseness vs. verbosity)?\n\n3. Is the improvement statistically significant? Accuracy gains (e.g., +1.3 points) are modest. Were significance tests conducted to rule out random variation?\n\nLIMITATIONS:\nWhile the hyperparameter t is claimed to be robust, its optimal value may vary across tasks (e.g., coding vs. math reasoning). No adaptive tuning strategy is proposed.\n\nResults for 32B models show diminishing returns in length reduction. The paper does not address whether ACM’s benefits hold for trillion-parameter models or on-device LLMs.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-21"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes Activation-Guided Consensus Merging (ACM), a plug-and-play method for merging large language models (LLMs) using layer-wise weighting based on mutual information (MI) between activations. Motivated by the observation that different layers contribute unequally to downstream task performance, ACM computes per-layer MI between the pre-trained and fine-tuned models and uses this to derive adaptive coefficients for merging. Unlike previous methods that require gradient computation or rely solely on activations from a single model, ACM introduces a symmetric MI-based strategy that balances parameter reuse and task-specific adaptation. Extensive experiments on Long-to-Short (L2S) reasoning and general merging tasks across various model scales (1.5B to 32B) demonstrate that ACM consistently improves accuracy while reducing redundancy, outperforming existing merging baselines.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n- The paper presents a well-motivated solution to the limitations of uniform merging strategies, introducing a mutual information (MI)-based method for assigning layer-wise coefficients in a principled way.\n\n- The proposed approach is training-free and computationally efficient, which is particularly appealing for real-world settings where retraining is impractical.\n\n- The experimental setup is comprehensive, covering a range of model sizes, architectures (e.g., Qwen, LLaMA), and task types including math, commonsense reasoning, and code generation. The method demonstrates consistent gains across these settings.\n\n- The use of MI as a signal for balancing redundancy and task specificity is conceptually clear and supported by both theoretical rationale and empirical evidence.\n\n- The method is compatible with existing merging frameworks such as TA and TIES, and appears to further enhance their performance, particularly in managing accuracy and response length trade-offs.\n\nWeaknesses:\n\n- The computation of MI-based merging coefficients relies on a calibration dataset and its clustering, which may introduce some sensitivity to the choice of data and its representativeness. A more detailed discussion of this point would be helpful.\n\n- While the method is designed to be plug-and-play, the requirement to extract activations from both the pre-trained and fine-tuned models introduces added complexity compared to simpler, static baselines.\n\n- The evaluation primarily focuses on long-to-short reasoning and mathematical tasks. Additional results on open-ended generation or instruction-following could help better understand the method’s generalization across a wider range of use cases.\n\nQUESTIONS:\nI list my concerns and questions in the \"weaknesses\" section. \n\nAside from those, I have one additional point of interest:\n- Given that the method computes MI over shared inputs, how well does it handle merging models trained on different tasks or exhibiting divergent behaviors? It would be helpful to discuss whether MI remains a meaningful signal in such heterogeneous scenarios.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper studies model merging over LLMs, with particular focus on the Long-to-Short task aiming to reduce the “reasoning trace” of reasoning models without waiving accuracy. To do so, they leverage task vectors, differences between finetuned models and their pretrained counterpart. The novelty of the paper stems in finding layer-specific merging coefficients that are a function of the mutual information between the activations of the task-specific model and the pretrained one.  The approach is extensively evaluated on long-to-short over Qwen2.5, Qwen2.5-Math and corresponding DeepSeek-R1 versions over GSM8K, MATH500, Minerva Math, Olympiadbench, College Math and AIME 2024. While LLama2-7B, MammoMath and CodeLlama-7b-hf over GSM8K, HellaSwag, MBPP-Pro are considered for general merging.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n\n- The paper is fairly clearly written, with clear methodology and detailed experimental setup. The code  is also provided for reproducibility and expected to be released upon acceptance.\n- The empirical evaluation is extensive, covering a diverse set of both reasoning and standard tasks for several different open state-of-the-art LLMs. The set of baselines is comprehensive enough.\n- The paper tackles an interesting and felt problem, as reducing the reasoning trace without waiving accuracy has immediate impactful applications, saving both computation and time.\n\n### Weaknesses\n\n- The gains seem marginal. When compared with AIM, the method obtains the same length reduction and an increase of 0.8% in accuracy. What is most striking, however, is that ACM-TIES with respect to standard TIES only results in +1% accuracy and +2.3% length on QWEN-1.5B and +0.08% accuracy on Lllama, despite requiring calibration data and added complexity.\n- The proposed approach seems to be incremental with respect to Sens-Merging [1], with the main difference stemming from a different way to compute the sensitivity of the parameters, as the proposed approach uses mutual information and Sens-merging using gradients. The paper only motivates the benefit of the approach with respect to Sens-merging as not requiring “complex gradient calculation”; I am not sure what complex here might mean. is it computationally expensive? error-prone?\n- Unconvincing motivation. I did not fully grasp why the mutual information between the activations of the pretrained model and those of the task-specific models is a meaningful quantity to be considered. Some additional explaination or theoretical/quantitative analysis might help understanding why this is a good idea in the first place.\n- Some minor inconsistencies.\n    - the paper claims: “In the specific case sigma is the activation function, the gradient is {0, 1}”; this is only true for some activation functions, e.g. ReLU, and does not hold in general.\n    - There are often inconsistenc spaces before/after equatons. I suggest removing all empty lines surrounding equation blocks in the source code.\n\n[1] Liu, Shuqi, et al. \"Sens-Merging: Sensitivity-Guided Parameter Balancing for Merging Large Language Models.\" *arXiv preprint arXiv:2502.12420* (2025).\n\nQUESTIONS:\n- L100 says “arithmetic merging operates fine-tuned features as directional task vectors in parameter space”. What does this mean? I suggest rephrasing\n- Is there a way to assess to what degree does the result depend on the quality/size of the calibration dataset?\n- How significant are the gains? being somewhat marginal, are these expected to be consistent across different seeds and setups?\n\nOverall, given the added requirements with respect to standard baselines, the method needs to yield convincingly better results while ensuring the significance of these ones.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-28"}

comments: SUMMARY:
The paper proposes Activation-Guided Consensus Merging (ACM) to improve the integration of multiple LLMs through model merging. Existing merging techniques often assume uniform importance across model layers, ignoring the diverse functional roles of different layers. ACM addresses this by using mutual information (MI) between layer-wise activations of pre-trained and fine-tuned models to assign layer-specific merging coefficients, giving more weight to divergent task-specific layers and less to redundant ones. This approach is plug-and-play, requiring no gradient computations or retraining.

STRENGTHS AND WEAKNESSES:
**Strengths**
* The paper writing is overall clear and easy to follow.
* The proposed model merging technique is well-motivated and reasonably principled.
* The experimental performance is well acceptable, even compared with strong baselines such as Deepseek r1 and Qwen-2.5.

**Weakness**
I don't see significant weakness.

QUESTIONS:
Do you assume that the fine-tuning data for all models is available? 
* If so, why would one apply model merging instead of simply fine-tuning on all the data, given that fine-tuning is usually not considered costly? 
* If not, can you provide an example of an application scenario where the datasets are not available, but the fine-tuned weights are?

LIMITATIONS:
I don't see obvious negative societal impact of this work.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03