Title: Activation-Informed Merging of Large Language Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: Model Merging

Activation‑Informed Merging

Large Language Models
Abstract: Model merging, a method that combines the parameters and embeddings of multiple fine-tuned large language models (LLMs), offers a promising approach to enhance model performance across various tasks while maintaining computational efficiency. This paper introduces Activation-Informed Merging (AIM), a technique that integrates the information from the activation space of LLMs into the merging process to improve performance and robustness. AIM is designed as a flexible, complementary solution that is applicable to any existing merging method. It aims to preserve critical weights from the base model, drawing on principles from continual learning (CL) and model compression. Utilizing a task-agnostic calibration set, AIM selectively prioritizes essential weights during merging. We empirically demonstrate that AIM significantly enhances the performance of merged models across multiple benchmarks. Our findings suggest that considering the activation-space information can provide substantial advancements in the model merging strategies for LLMs with up to 40% increase in benchmark performance. Our code is publicly available at https://github.com/ahnobari/ActivationInformedMerging


Peer reviews:
comments: SUMMARY:
This paper proposes Activation-Informed Merging (AIM), a simple yet effective technique to enhance model merging for fine-tuned LLMs by leveraging activation  information from the base model. By computing input activation magnitudes from a small calibration set, AIM identifies salient channels and constrains their updates during merging, thereby preserving critical base capabilities.

STRENGTHS AND WEAKNESSES:
**strength**

1. paper writing is clear and easy to follow

2. the method be plugged into many merging methods with minimal overhead.

3. The method is simple  and efficient. And it consistently improves performance across benchmarks 

**weakness**
1. All experiments are conducted on  a single model (LLaMA-2-13B). It remains unclear whether AIM is robust across different model families and scales

2.  Most evaluations are limited to merging two or three models . It's unclear how AIM scales when merging larger pools of fine-tuned experts in diverse domains.

QUESTIONS:
If a particular channel exhibits high activation or gradient magnitude, it may be critical for both the base and fine-tuned models. Would overly constraining its update risk suppressing important adaptations from the fine-tuned model and thereby hurt performance?

LIMITATIONS:
The paper does not include a Limitations section. The authors should discuss potential weaknesses of the method, particularly its generalization ability across models, beyond the tested settings.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-29

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Activation-Informed Merging (AIM), a complementary technique for combining multiple fine-tuned Large Language Models (LLMs) that originate from the same base model. Drawing from continual learning principles to avoid catastrophic forgetting, AIM uses a task-agnostic calibration dataset to identify and protect the most critical weights of the base model by analyzing its activation space. The authors demonstrate that applying AIM to existing merging methods significantly enhances performance across various benchmarks by up to 40%. The work also proposes a new evaluation metric, hypervolume gain, to assess the multi-objective improvements of merged models.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n- The paper combines ideas from model compression (activation-aware quantization) and continual learning, and applied to model merging. This offers a fresh perspective in a field dominated by weight-space-only methods.\n- The proposed AIM method demonstrates performance gains, with improvements of up to 40% reported on standard benchmarks\n- Introduces an evaluation metric, hypervolume gain, which captures the multi-objective nature of merging.\n\n## Weaknesses\n- All experiments are conducted on a single model family (Llama-2-13b). The paper does not show if the findings generalize to other model architectures or scales\n- The method relies on a calibration dataset, adding an extra step not required by pure weight-space methods. While claimed to be robust, this is still an added dependency.\n\nQUESTIONS:\n1. Your experiments provide evidence for AIM's effectiveness, but they are exclusively focused on models fine-tuned from the Llama-2-13b architecture. Could you comment on why you expect these results to generalize to other model architectures (e.g., Mistral, Gemma) and scales?\n2. Have you considered how activation information from the fine-tuned expert models themselves could be used to inform the merge? For instance, how would you handle a scenario where the same neurons are salient for different tasks in conflicting ways?\n3. How sensitive is the resulting performance of a merged model to the domain and quality of the calibration data? For example, how would AIM perform if the calibration set was highly domain-specific (e.g., only legal or medical texts) and misaligned with the general pre-training distribution?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a model merging approach to enhance large language model (LLM) merging by integrating activation-space information. Unlike traditional methods focused solely on weight space, the proposed method leverages a task-agnostic calibration set and principles from continual learning to selectively preserve critical base-model weights, mitigating catastrophic forgetting while integrating fine-tuned knowledge. Empirical results demonstrate that AIM consistently improves merged model performance across benchmarks, achieving up to a 40% increase in task accuracy.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n- The paper is well-written and logically structured, making it easy to follow the research flow.\n- Unlike previous merging models that relied on simple weighted averaging in a defined space, this work proposes quantization and pruning methodologies and demonstrates their significance.\n- While calibration datasets are required, the authors used only a small set of 256 samples, showing that the method is easily applicable.\n- Extensive experiments across multiple merging scenarios were conducted to analyze the gains from applying AIM, and most cases showed positive performance improvements. Notably, Table 1​ (MMLU benchmark) demonstrates superior performance across all scenarios, and Figure 2​ shows a Pareto-efficient upward trend, both validating the proposed method’s effectiveness.\n\n### Weaknesses\n- Although Section 3.3​ claims that 256 calibration samples are sufficient, it remains unclear how the method would perform if the calibration data is biased. Experimental validation of robustness with varying calibration dataset sizes would strengthen the claims.\n- The merging experiments were limited to models based on Llama-2-13B​. It would be beneficial to test the approach on smaller models or those fine-tuned from different base models to verify generalizability.\n\nQUESTIONS:\nIn Figure 3​, the experiments on varying ω show HV gains even when ω=0 in most cases, which is intriguing. However, the Ties Merging​ scenario shows a decline. What could explain this discrepancy?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Activation-Informed Merging (AIM) for merging fine-tuned LLMs derived from the same pre-trained base. AIM enhances model merging by leveraging the activation space to guide which weights should be preserved during merging. The method aims to prevent performance degradation, particularly the \"catastrophic forgetting\" of the base model’s general abilities, by constraining updates to the most salient (activation-sensitive) weights. The authors conduct empirical evaluations across multiple benchmarks (math, code, instruction following) and demonstrate that AIM consistently boosts the performance of merged models, sometimes by as much as 40% in benchmark metrics.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The paper is well-grounded in the literature, thoughtfully connecting AIM to both continual learning (notably, weight regularization to prevent catastrophic forgetting) and recent advances in model compression (activation-aware quantization).\n2. Experiments cover five recent merging methods, three types of fine-tuned LLM experts (math, code, instruction), and six task benchmarks. The performance is measured both in standard metrics and a new hypervolume-based multi-task evaluation.\n\nWeaknesses:\n1. The need for access to activations and a representative calibration set may limit the method’s immediate applicability in proprietary or privacy-sensitive domains.\n2. The novelty is somewhat incremental: AIM is an adaptation of activation-based ideas from compression to the merging context. This is clearly articulated, though.\n3. Only tested on one model family and size (Llama-2-13B).\n\nQUESTIONS:\n1. How does AIM perform on other model families (GPT, Mistral) or different model sizes? The evaluation is limited to Llama-2-13B.\n2. While you cite robustness from other work, have you empirically validated sensitivity to calibration dataset choice in the merging context?\n3. Have you considered other importance metrics beyond activation magnitude (e.g., gradient-based measures, Fisher information)?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-08"}