一个适配模型能胜任一切吗？客户支持大语言模型的微调策略选择

# Can One Adapted Model Do It All? Fine-Tuning Strategy Selection for Customer Support LLMs
Source: [https://arxiv.org/html/2609.27262](https://arxiv.org/html/2609.27262)
###### Abstract

Production customer\-support systems often require LLMs to support multiple skills, such as intent classification, question answering, summarization, or tool\-use decisions\. A central deployment question is whether these skills should be handled by separate task\-specialist models or by a single model trained through multi\-task training, sequential updates, or model merging\. We study this question using thirteen models spanning five families \(Qwen3, Qwen3\.5, Gemma\-3, Llama\-3\.1, and Mistral\) from 0\.6B to 32B parameters across eight customer\-support datasets, spanning four public and four proprietary datasets with approximately 74\.5k training and 8\.7k evaluation samples\. Under a fixed training protocol, we train more than 200 checkpoints\. Our experiments reveal that multi\-task full fine\-tuning is the strongest operational default at*every*model size we test\. Specialist models are strong on their target tasks but often degrade sharply off\-task, making reliable routing important\. Sequential Low\-Rank Adaptation \(LoRA\) preserves earlier skills better than sequential full fine\-tuning, while merging a specialist with its base model improves off\-task robustness with limited same\-task loss for larger models\. We conclude with practical guidelines for selecting fine\-tuning strategies in real\-world settings\.

## 1Introduction

Production conversational assistants in customer support are expected to perform multiple tasks, such as identifying a customer’s intent, answering knowledge\-base questions, summarizing a conversation, or deciding whether to call an external tool[Zhang et al\. \(2025\)](https://arxiv.org/html/2609.27262#bib.bib26);[Laskar et al\. \(2023\)](https://arxiv.org/html/2609.27262#bib.bib24);[Laskar et al\. \(2025\)](https://arxiv.org/html/2609.27262#bib.bib23);[Alkhouli et al\. \(2025\)](https://arxiv.org/html/2609.27262#bib.bib25)\. Real\-world deployments favor cost\-effective, smaller LLMs, which lack the general\-purpose capability of larger ones and therefore must be adapted to target tasks via fine\-tuning[Fu et al\. \(2024\)](https://arxiv.org/html/2609.27262#bib.bib22)\. This raises a practical design question: should practitioners train a separate model for each skill, or one model that performs all skills?

This choice has direct deployment implications\. Separate specialist models can provide strong task\-specific performance, but they require more storage, more endpoints, and reliable routing to the appropriate specialist model[Chen et al\. \(2023\)](https://arxiv.org/html/2609.27262#bib.bib13);[Ding et al\. \(2024\)](https://arxiv.org/html/2609.27262#bib.bib14);[Ong et al\. \(2025\)](https://arxiv.org/html/2609.27262#bib.bib16)\. In contrast, models fine\-tuned across multiple tasks can serve many tasks through a single checkpoint\. However, updating one task may change the behavior of others\. While other strategies can reduce some of these costs, they may also introduce different trade\-offs\. For instance, parameter\-efficient fine\-tuning techniques like LoRA\([Hu et al\., 2022](https://arxiv.org/html/2609.27262#bib.bib7)\)keep a shared base model and train small task\-specific adapters that can reduce storage and update cost; they may fail to match the performance of full fine\-tuning\. Techniques like sequential fine\-tuning[Fu et al\. \(2022\)](https://arxiv.org/html/2609.27262#bib.bib15)can be useful when new tasks are added over time, but they can degrade performance on earlier tasks\. Post\-hoc model merging methods\([Wortsman et al\., 2022](https://arxiv.org/html/2609.27262#bib.bib8);[Ilharco et al\., 2023](https://arxiv.org/html/2609.27262#bib.bib9);[Yu et al\., 2024](https://arxiv.org/html/2609.27262#bib.bib11)\)can combine trained checkpoints without additional fine\-tuning, but their ability to preserve multiple customer\-support skills remains unclear\.

Despite these trade\-offs, practitioners may choose among these strategies using informal rules rather than controlled evidence\. Prior work has studied multi\-task learning\([Raffel et al\., 2020](https://arxiv.org/html/2609.27262#bib.bib12);[Sanh et al\., 2022](https://arxiv.org/html/2609.27262#bib.bib17)\), adapter\-based fine\-tuning\([Hu et al\., 2022](https://arxiv.org/html/2609.27262#bib.bib7);[Pfeiffer et al\., 2021](https://arxiv.org/html/2609.27262#bib.bib21)\), continual learning\([Parisi et al\., 2019](https://arxiv.org/html/2609.27262#bib.bib5);[De Lange et al\., 2021](https://arxiv.org/html/2609.27262#bib.bib6)\), and model merging\([Wortsman et al\., 2022](https://arxiv.org/html/2609.27262#bib.bib8);[Yadav et al\., 2023](https://arxiv.org/html/2609.27262#bib.bib10)\)separately\. However, these methods are rarely compared under the same training setup, model family, and evaluation protocol, particularly on real\-world customer\-support workloads that require multiple deployment\-relevant skills\. This makes it difficult to know whether performance differences come from the strategy itself or from changes in data, models, or evaluation\.

To this end, we present a controlled comparison of different fine\-tuning strategies for customer support use cases that covers: task\-specific full fine\-tuning, LoRA adaptation, multi\-task fine\-tuning, sequential fine\-tuning, and model merging\. Our experiments span thirteen models across five families and parameter counts from 0\.6B to 32B, yielding over 200 trained models\. Our key insights are:

\(i\) Multi\-task full fine\-tuning is the strongest default across the thirteen models

\(ii\) Specialist models often perform well on their target task but degrade sharply on others, which matters when routing is imperfect;

\(iii\) Sequential full fine\-tuning substantially hurts earlier skills, while LoRA can mitigate;

\(iv\) Mixing a specialist with its base model retains performance on non\-fine\-tuned tasks with only a small loss on the target task;

Based on these findings, we provide practical recommendations for choosing a strategy based on task similarity, model size, and update frequency\.

## 2Related Work

Fine\-tuning and parameter\-efficient adaptation\.Full fine\-tuning adapts all model parameters and can provide strong task\-specific performance, but is computationally expensive and may reduce general capabilities\([Devlin et al\., 2019](https://arxiv.org/html/2609.27262#bib.bib3);[Raffel et al\., 2020](https://arxiv.org/html/2609.27262#bib.bib12);[Xu et al\., 2026](https://arxiv.org/html/2609.27262#bib.bib2)\)\. Parameter\-efficient methods such as LoRA instead update a small set of parameters while keeping the base model frozen\([Hu et al\., 2022](https://arxiv.org/html/2609.27262#bib.bib7)\)\. We compare both approaches in this work\.

Multi\-task learning and task specialization\.Multi\-task learning trains one model across tasks and can improve data efficiency and generalization\([Raffel et al\., 2020](https://arxiv.org/html/2609.27262#bib.bib12);[Sanh et al\., 2022](https://arxiv.org/html/2609.27262#bib.bib17)\), although its effectiveness depends on task relatedness, data composition, and model capacity\([Wei et al\., 2022](https://arxiv.org/html/2609.27262#bib.bib18);[Ouyang et al\., 2022](https://arxiv.org/html/2609.27262#bib.bib20)\)\. Task\-specific fine\-tuning may perform better in\-task but generalize poorly to other tasks\([Wang et al\., 2024](https://arxiv.org/html/2609.27262#bib.bib19)\), creating a trade\-off between specialization and maintaining a limited number of deployment endpoints\.

Sequential updates and catastrophic forgetting\.Sequential adaptation supports tasks added over time but can cause catastrophic forgetting, in which later updates degrade earlier capabilities\([Goodfellow et al\., 2013](https://arxiv.org/html/2609.27262#bib.bib4);[Parisi et al\., 2019](https://arxiv.org/html/2609.27262#bib.bib5);[De Lange et al\., 2021](https://arxiv.org/html/2609.27262#bib.bib6)\)\. This risk is especially relevant for models repeatedly updated for new customer\-support tasks\.

Model merging\.AdapterFusion\([Pfeiffer et al\., 2021](https://arxiv.org/html/2609.27262#bib.bib21)\)and LoRAHub\([Huang et al\., 2024](https://arxiv.org/html/2609.27262#bib.bib41)\)learn to combine task\-specific adapters using supervision or few\-shot examples\. Post\-hoc merging instead combines independently fine\-tuned parameters without additional training\([Wortsman et al\., 2022](https://arxiv.org/html/2609.27262#bib.bib8);[Ilharco et al\., 2023](https://arxiv.org/html/2609.27262#bib.bib9)\), but conflicting task updates can cause negative transfer\([Yadav et al\., 2023](https://arxiv.org/html/2609.27262#bib.bib10)\)\. In this paper, we investigate post\-hoc model merging that does not require any re\-training\.

To our knowledge, these adaptation strategies have not been systematically compared under a unified protocol across customer\-support tasks yet\.

## 3Methodology

### 3\.1Adaptation Strategies

We compare several adaptation strategies for production customer\-support LLMs under a fixed model training and evaluation protocol\. Specifically, our methodology includes single\-task and multi\-task fine\-tuning, parameter\-efficient fine\-tuning with LoRA, sequential fine\-tuning for continual\-task updates, and post\-hoc model merging of the trained model\(s\) with the base model or between them\. Below, we discuss various adaptation strategies that we study\.

Single\-task and multi\-task adaptation\.We compare task\-specialist models with unified multi\-task models\. In the single\-task setting, we train one model per task and evaluate it on its target task alongside other remaining tasks in the evaluation suite\. This measures task specialization as well as generalization and robustness\. In the multi\-task setting, we train one model on a training set that covers all tasks within an evaluation suite\. This produces one single checkpoint intended to serve multiple customer\-support features\.

Full and parameter\-efficient fine\-tuning\.We evaluate both full fine\-tuning and parameter\-efficient fine\-tuning\. Full fine\-tuning updates all model parameters, whereas parameter\-efficient fine\-tuning uses LoRA adapters while keeping the base model fixed\. We apply these strategies in both single and multi\-task settings to compare target\-task performance, as well as cross\-task retention\.

Sequential fine\-tuning and continual adaptation\.To simulate settings where new skills are added over time, we fine\-tune models sequentially across tasks\. We compare sequential full fine\-tuning with sequential LoRA and evaluate the final checkpoint on all tasks\. This setup allows us to measure catastrophic forgetting, i\.e\., whether adapting to later tasks degrades performance on earlier ones\.

Model merging\.Finally, we investigate model merging on independently trained task specialists by merging them with their original base model as well as with themselves\. This assesses whether merging can preserve in\-task gains while maintaining robustness on tasks outside the specialists training distribution\. In this work, we evaluate post\-hoc TIES merging\([Yadav et al\., 2023](https://arxiv.org/html/2609.27262#bib.bib10)\)

### 3\.2Datasets

We evaluate different fine\-tuning strategies across diverse customer\-support datasets, covering tasks from public academic benchmarks and real\-world proprietary benchmarks\. We discuss these benchmarks below \(we also provide the prompts for all tasks in Appendix[A](https://arxiv.org/html/2609.27262#A1)\)\.

#### 3\.2\.1Public Academic Suite

The public suite comprises four tasks \(total56,75256\{,\}752training and6,6276\{,\}627evaluation samples\):

Dialogue Summarization \(DS\)\.Conversation summarization is an important feature in customer support[Laskar et al\. \(2023\)](https://arxiv.org/html/2609.27262#bib.bib24);[Laskar et al\. \(2024\)](https://arxiv.org/html/2609.27262#bib.bib1)\. For this, we use the SAMSum\([Gliwa et al\., 2019](https://arxiv.org/html/2609.27262#bib.bib28)\)dataset \(14,73114\{,\}731train,1,6371\{,\}637eval\)\.

Question Answering \(QA\)\.We use WixQA\([Cohen et al\., 2025](https://arxiv.org/html/2609.27262#bib.bib30)\)for retrieval\-grounded QA over a product knowledge base, with retrieved articles provided as context and answers required to cite evidence\. The*simulated*split is used for training \(200200\) and the*expert\-written*split for evaluation \(200200\)\. Note that WixQA is simultaneously low\-resource and distribution\-shifted: the training split is simulated while the evaluation split is expert\-written, and the two differ in document length, formatting, and citation style\. This makes it a challenging benchmark for evaluation\.

Chatbot Response \(CS\)\.We use the When2Call\([Ross et al\., 2025](https://arxiv.org/html/2609.27262#bib.bib29)\)\(15,00015\{,\}000train;3,9523\{,\}952eval\) dataset to investigate whether the model can correctly decide when to refrain from generating hallucinated answers, ask a follow\-up question, or call a tool while responding to a user in a conversation\.

Table 1:Same\-task performance under single\-task \(ST\) and multi\-task \(MT\) fine\-tuning, full\-parameter and LoRA, plus the*Zero\-shot*base per model\. ST models are scored on the task they were trained for\.
#### 3\.2\.2Proprietary Internal Suite

The internal suite also comprises four tasks, which consist of real\-world customer support transcripts \(17,79117\{,\}791training and2,0862\{,\}086evaluation examples in total\) collected from \[REDACTED\]\. The tasks are described below\.

Action Items \(AI\)\.This task focuses on producing a set of actionable follow\-up tasks from a call transcript that should be completed after the call ends \(5,755 train and 646 eval\)\( \)\.

Meeting Summarization \(MS\)\.This task requires generating a concise summary of a business meeting according to specific constraints, such as the desired length \(long, medium, or short\) or format \(for example, bullet points\)\. It contains7,8297\{,\}829training and925925evaluation samples\.

Call Purpose \(CP\)\.This task requires the classification of the main purpose of a call into one of the predefined categories\. For the call purpose, the model is asked to provide the call purpose category with an explanation \(1,5961\{,\}596train,200200eval\)\.

Call Outcome \(CO\)\.This task involves identifying the primary outcome of a conversation by assigning it to one of the predefined call outcome categories without any additional text \(2,6112\{,\}611train,315315eval\)\.

### 3\.3Models

We evaluate thirteen models from five families, spanning 0\.6B–32B parameters: Qwen3\-0\.6B, 1\.7B, 4B, 8B, 14B, and 32B\([Yang et al\., 2025](https://arxiv.org/html/2609.27262#bib.bib32)\); Qwen3\.5\-2B, 4B, and 9B333[https://huggingface\.co/Qwen/Qwen3\.5\-9B](https://huggingface.co/Qwen/Qwen3.5-9B); Gemma\-3\-1B and 4B\([Gemma Team, 2025](https://arxiv.org/html/2609.27262#bib.bib38)\); Llama\-3\.1\-8B\([Grattafiori and others, 2024](https://arxiv.org/html/2609.27262#bib.bib39)\); and Mistral\-7B\-Instruct\-v0\.3\([Jiang et al\., 2023](https://arxiv.org/html/2609.27262#bib.bib40)\)\. The Qwen3 models are text\-only, whereas Qwen3\.5 is multimodal; the matched 4B variants help separate family effects from model size\. All models were downloaded from Hugging Face\([Wolf et al\., 2019](https://arxiv.org/html/2609.27262#bib.bib31)\)\. The hyperparameters that we use for training and inference, alongside implementation details, are given in Appendix[C](https://arxiv.org/html/2609.27262#A3)\.

## 4Results and Discussion

In this section, we present our experimental results\. For brevity, we report the classification tasks: Intent Classification \(IC\), Call Outcome \(CO\), and Call Purpose \(CP\) using exact match, and the text generation tasks: Dialogue Summarization \(DS\), Meeting Summarization \(MS\), Action Items \(AI\), Question Answering \(QA\), and Chatbot Response \(CR\) using ROUGE\-1\([Lin, 2004](https://arxiv.org/html/2609.27262#bib.bib36)\)\. We additionally evaluate the generation tasks using BERTScore[Zhang et al\. \(2020\)](https://arxiv.org/html/2609.27262#bib.bib42)and an LLM judge\.

### 4\.1Main Results

Table[1](https://arxiv.org/html/2609.27262#S3.T1)compares zero\-shot, single\-task \(ST\), and multi\-task \(MT\) adaptation for seven representative models \(Table[6](https://arxiv.org/html/2609.27262#A1.T6)in Appendix[B](https://arxiv.org/html/2609.27262#A2)reports the remaining six\)\. We observe that ST\-Full and MT\-Full perform similarly for most models\. More specifically, ST\-Full leads on the public suite by 1\.2 points on average and at most 2\.8 points\. On the internal suite, all models have gaps within±2\.5\\pm 2\.5points\. MT\-LoRA is generally close to MT\-Full but trails on the internal suite by 5\.4 points for Qwen3\-0\.6B, 4\.5 for Qwen3\-1\.7B, and 4\.1 for Qwen3\-8B\. Thus, its limitations are model\-specific rather than governed by a consistent size threshold\.

Our additional analysis on the internal suite’s call outcome task reveals that fine\-tuned models almost always produce valid call\-outcome labels, indicating that exact\-match differences across models primarily reflect incorrect label selection rather than formatting\. For example, Qwen3\-1\.7B ST\-Full and MT\-Full have similar validity rates \(96\.8% vs\. 96\.2%\) despite differing substantially in accuracy \(76\.2 vs\. 54\.6\)\.

The strongest overall model in our evaluation, Qwen3\.5\-9B MT\-Full, is further compared with some proprietary zero\-shot baselines on both suites in Figure[1](https://arxiv.org/html/2609.27262#S4.F1)\. We observe that the Qwen3\.5\-9B MT\-Full model consistently outperforms the zero\-shot models\.

Takeaway 1\.*MT\-Full is the best operational default for deployment: it performs close to task specialists for most models while requiring only one checkpoint and no router\. Specialist models should be adopted only when target\-suite validation demonstrates a meaningful advantage\.*

Figure 1:Comparison between closed zero\-shot models and the strongest fully fine\-tuned model, Qwen3\.5\-9B MT\-Full\.![Refer to caption](https://arxiv.org/html/2609.27262v1/fig_crosstask.png)Figure 2:Cross\-task performance of single\-task models: Qwen3\-8B on the public suite and Qwen3\.5\-9B on the internal suite\. Rows are the task a specialist was trained on, and columns are the tasks on which it is evaluated\.Table 2:Sequential fine\-tuning: IC→\\rightarrowQA→\\rightarrowDS→\\rightarrowCS for Qwen3\-8B on the public suite, and AI→\\rightarrowCO→\\rightarrowMS→\\rightarrowCP for Qwen3\.5\-9B on the internal suite\.
### 4\.2Cross\-Task Robustness

In production, imperfect routing can send a specialist model a request meant for another model\. Figure[2](https://arxiv.org/html/2609.27262#S4.F2)shows that top ST specialists degrade sharply off\-task\. On the public suite, the Qwen3\-8B chatbot\-response specialist scores 42\.3 on its own task but drops to 18\.1 on QA and 14\.4 on dialogue summarization\. On the internal suite, the action\-items and meeting\-summary specialists score 57\.1 and 48\.3 on each other’s tasks\. Nonetheless, the label\-producing call\-outcome specialist performs very poorly on generation tasks \(e\.g\., scores below 5\.0 across generation tasks\)\.

These findings suggest that transfer is stronger between tasks with similar outputs\. Thus, output formats may predict cross\-task robustness better than topic similarity\. Specialists should therefore be deployed only with reliable routing or across tasks with compatible output requirements\.

Takeaway 2\.*Specialists are strong in\-task but can fail badly off\-task\. Do not deploy them unless routing is reliable\.*

### 4\.3Sequential Fine\-Tuning

To simulate incremental skill addition, we train Qwen3\-8B in the order IC→\\rightarrowQA→\\rightarrowDS→\\rightarrowCS and Qwen3\.5\-9B in the order AI→\\rightarrowCO→\\rightarrowMS→\\rightarrowCP\. Table[2](https://arxiv.org/html/2609.27262#S4.T2)reports the final checkpoint after all four stages\. Sequential full fine\-tuning causes substantial forgetting for ST\-Full; intent classification falls from 99\.5 to 25\.9 and action items from 61\.3 to 46\.1\. Sequential LoRA preserves these tasks better, retaining 78\.6 and 57\.8, respectively, although it trails the strongest non\-sequential model\.

We further analyze the results by reversing the order and observe that earlier tasks suffer the most\. Under reversed sequential full fine\-tuning, the now\-last intent\-classification task retains 98\.9, while the now\-first chatbot\-response task falls to 27\.4 from 42\.4\. Sequential LoRA again retains the first internal task better than sequential full fine\-tuning \(in the reverse order: 53\.0 vs\. 39\.0\)\. Thus, sequential LoRA is safer, and the most critical task should be trained last\.

Figure 3:Cross\-task results for Qwen3\.5\-9B on the internal suite after merging a specialist with its base atα=0\.5\\alpha=0\.5, compared with the unmerged specialist and the zero\-shot base\.Table 3:Mean effect of merging each specialist with its base model \(α=0\.5\\alpha=0\.5\) across public and internal suites\.Figure 4:TIES merging ofNNpublic\-suite specialists compared with multi\-task fine\-tuning\.Takeaway 3\.*Sequential LoRA is safer than sequential full fine\-tuning, but retraining across multiple tasks or adding a new specialist is preferable when feasible\.*

### 4\.4Model Merging

We evaluate two post\-hoc strategies: merging a specialist with its base model and merging multiple specialists\. To merge a specialist with its base, for each ST checkpoint, we computeθmerged=α​θFT\+\(1−α\)​θbase\\theta\_\{\\text\{merged\}\}=\\alpha\\theta\_\{\\text\{FT\}\}\+\(1\-\\alpha\)\\theta\_\{\\text\{base\}\}, where largerα\\alphafavors the specialist \(to ensure both off\-task and on\-task robustness, we primarily selectα\\alpha=5\)\. Table[3](https://arxiv.org/html/2609.27262#S4.T3)shows that this approach consistently improves off\-task robustness\. For models≥\\geq4B, these gains come with at most a 2\.6\-point same\-task loss, while Qwen3\.5\-9B improves both same\- and off\-task performance\. Figure[3](https://arxiv.org/html/2609.27262#S4.F3)provides its task\-level results\. Table[4](https://arxiv.org/html/2609.27262#S4.T4)further illustrates the trade\-off between specialization and robustness\. Asα\\alphaincreases from 0\.3 to 0\.7, off\-task performance decreases on both suites, while in\-task performance increases\. Thus,α=0\.3\\alpha=0\.3favors robustness andα=0\.7\\alpha=0\.7favors specialization\.

Table 4:Merge\-with\-base results for differentα\\alphavalues averaged over the five core models of different sizes \(Qwen3 0\.6B, 1\.7B, 4B, 8B and Qwen3\.5\-9B\)\.Merging multiple specialists does not match joint MT fine\-tuning and fails to improve average score as more specialists \(e\.g\., from 3 to 4\) are added \(Figure[4](https://arxiv.org/html/2609.27262#S4.F4)\)\.

Takeaway 4\.*Merging a specialist with its base is a cheap robustness fix, while merging several specialists together cannot substitute MT\-Full\.*

### 4\.5Beyond Lexical Overlap

We choose ROUGE\-1 as a deterministic metric under which all strategies could be compared identically since our goal is the*relative*effect of the adaptation strategy, not state\-of\-the\-art result\. Thus, we additionally evaluate the three generation tasks in each suite using BERTScore\([Zhang et al\., 2020](https://arxiv.org/html/2609.27262#bib.bib42)\)and a Gemini\-3\-Flash judge that rates responses from 1–5\. Table[5](https://arxiv.org/html/2609.27262#S4.T5)shows consistent conclusions across metrics\. All fine\-tuned strategies outperform zero\-shot, while ST\-Full and MT\-Full remain comparable: the LLM\-judge differences are very minor, and BERTScore favors MT\-Full on the public suite but ST\-Full marginally on the internal suite\. Thus, the generation quality of multi\-task models and specialists is not specific to lexical overlap\.

Table 5:Average scores over generation tasks for Qwen3\-8B\.
### 4\.6Deployment Considerations

Post\-training strategy selection should reflect endpoint limits, update frequency, and routing reliability\. MT\-Full is preferred when all task data are available, as it achieves performance comparable to task\-specific models\. Specialists are appropriate only when they offer a substantial advantage and routing is reliable\. When MT training is infeasible, sequential LoRA supports task updates with less forgetting, while merging specialists with their base improves off\-task robustness\. Appendix[E](https://arxiv.org/html/2609.27262#A5)further reports training times across model sizes\.

## 5Conclusion

We conducted a controlled comparison of post\-training strategies across thirteen customer\-support LLMs from five families and 0\.6B–32B parameters\. Single\-task and multi\-task full fine\-tuning perform similarly for most models, with no consistent size or capability threshold determining which is better\. More substantial differences emerge beyond same\-task performance: specialists can fail sharply off\-task, sequential full fine\-tuning causes considerable forgetting, and LoRA better preserves earlier skills\. Merging a specialist with its base improves robustness, whereas merging multiple specialists does not reliably replace joint training\. Overall, post\-training strategies should be evaluated through cross\-task behavior and skill retention, rather than model scale or in\-task performance alone\.

## Limitations

##### Model coverage\.

The study covers thirteen bases across five families \(Qwen3, Qwen3\.5, Gemma\-3, Llama\-3\.1, Mistral\) from 0\.6B to 32B\. Coverage is nonetheless uneven: the full strategy grid, including LoRA and merging variants, is run for a seven\-model core, while the remaining models contribute single\-task and multi\-task full fine\-tuning only\. All bases are instruction\-tuned; we do not test non\-instruct checkpoints or models above 32B\.

##### Single language\.

All training and evaluation datasets in both suites are English\-only, so all conclusions in this paper are English\-only conclusions; whether the ST\-versus\-MT balance, the off\-task collapse of specialists, or the forgetting behaviour of sequential updates hold under multilingual customer\-support conversations is untested here\. Extending to multilingual datasets is therefore left as future work\.

##### α\\alphachoice\.

Main\-text merge\-with\-base tables useα=0\.5\\alpha=0\.5for evaluation consistency across all 32 merge configurations\. Section[4\.4](https://arxiv.org/html/2609.27262#S4.SS4)additionally reportsα∈\{0\.3,0\.7\}\\alpha\\in\\\{0\.3,0\.7\\\}as deployment operating points, and Appendix[D](https://arxiv.org/html/2609.27262#A4)gives the full sweep\.

##### Metric scope\.

We choose ROUGE\-1 as a deterministic metric for generation tasks\. This is done so that all strategies can be compared identically since our goal is the*relative*effect of the adaptation strategy, not the state\-of\-the\-art result\. Nonetheless, generation tasks are additionally evaluated by BERTScore and an LLM judge \(§[4\.5](https://arxiv.org/html/2609.27262#S4.SS5)\)\.

##### Reproducibility\.

Half of this study runs on public data and is independently reproducible\. We release the training and inference configurations for every run, the exact task prompts, the preprocessing scripts that build the four public datasets from their published sources, and the training and evaluation code, including the scoring implementations\. The four internal datasets consist of PII\-redacted enterprise call transcripts and cannot be released; the internal results are therefore reported as supporting production evidence rather than as an independently verifiable benchmark\.

## Broader Impact and Ethics Statement

1\.Helping practitioners pick the right strategy reduces wasted training compute and the energy footprint associated with it\. The merge\-with\-base finding suggests many single\-task fine\-tunings can be made cross\-task robust with no additional training, leading to direct compute savings\. To help practitioners identify compute requirements, we also report the training time in Appendix[E](https://arxiv.org/html/2609.27262#A5)across model sizes\.

2\.Our experiments use real customer\-call transcripts, PII\-redacted, in the proprietary internal suite\. We do not release the proprietary models or training data\.

## References

- Alkhouliet al\.\(2025\)T\. Alkhouli, K\. Margatina, J\. Gung, R\. Shu, C\. Zaghi, M\. Sunkara, and Y\. ZhangCONFETTI: conversational function\-calling evaluation through turn\-level interactions\.InProceedings of the 63rd Annual Meeting of the Association for Computational Linguistics \(Volume 1: Long Papers\),pp\. 7993–8006\.Cited by:[§1](https://arxiv.org/html/2609.27262#S1.p1.1)\.
- Chenet al\.\(2023\)L\. Chen, M\. Zaharia, and J\. ZouFrugalgpt: how to use large language models while reducing cost and improving performance\.arXiv preprint arXiv:2305\.05176\.Cited by:[§1](https://arxiv.org/html/2609.27262#S1.p2.1)\.
- Cohenet al\.\(2025\)D\. Cohen, L\. Burg, S\. Pykhnivskyi, H\. Gur, S\. Kovynov, O\. Atzmon, and G\. BarkanWixQA: a multi\-dataset benchmark for enterprise retrieval\-augmented generation\.arXiv preprint arXiv:2505\.08643\.Cited by:[§3\.2\.1](https://arxiv.org/html/2609.27262#S3.SS2.SSS1.p4.1)\.
- De Langeet al\.\(2021\)M\. De Lange, R\. Aljundi, M\. Masana, S\. Parisot, X\. Jia, A\. Leonardis, G\. Slabaugh, and T\. TuytelaarsA continual learning survey: defying forgetting in classification tasks\.IEEE Transactions on Pattern Analysis and Machine Intelligence44\(7\),pp\. 3366–3385\.Cited by:[§1](https://arxiv.org/html/2609.27262#S1.p3.1),[§2](https://arxiv.org/html/2609.27262#S2.p3.1)\.
- Devlinet al\.\(2019\)J\. Devlin, M\. Chang, K\. Lee, and K\. ToutanovaBert: pre\-training of deep bidirectional transformers for language understanding\.InProceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 \(long and short papers\),pp\. 4171–4186\.Cited by:[§2](https://arxiv.org/html/2609.27262#S2.p1.1)\.
- Dinget al\.\(2024\)D\. Ding, A\. Mallick, C\. Wang, R\. Sim, S\. Mukherjee, V\. Rühle, L\. Lakshmanan, and A\. H\. AwadallahHybrid llm: cost\-efficient and quality\-aware query routing\.InInternational Conference on Learning Representations,Vol\.2024,pp\. 41348–41366\.Cited by:[§1](https://arxiv.org/html/2609.27262#S1.p2.1)\.
- Frankleet al\.\(2020\)J\. Frankle, G\. K\. Dziugaite, D\. Roy, and M\. CarbinLinear mode connectivity and the lottery ticket hypothesis\.InInternational Conference on Machine Learning \(ICML\),Cited by:[Appendix D](https://arxiv.org/html/2609.27262#A4.p2.1)\.
- Fuet al\.\(2022\)X\. Fu, C\. Chen, M\. T\. R\. Laskar, S\. B\. Tn, and S\. Corston\-OliverAn effective, performant named entity recognition system for noisy business telephone conversation transcripts\.InProceedings of the Eighth Workshop on Noisy User\-generated Text \(W\-NUT 2022\),pp\. 96–100\.Cited by:[§1](https://arxiv.org/html/2609.27262#S1.p2.1)\.
- Fuet al\.\(2024\)X\. Fu, M\. T\. R\. Laskar, E\. Khasanova, C\. Chen, and S\. TnTiny titans: can smaller large language models punch above their weight in the real world for meeting summarization?\.InProceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies \(Volume 6: Industry Track\),pp\. 387–394\.Cited by:[§1](https://arxiv.org/html/2609.27262#S1.p1.1)\.
- Gemma Team \(2025\)Gemma TeamGemma 3 technical report\.arXiv preprint arXiv:2503\.19786\.Cited by:[§3\.3](https://arxiv.org/html/2609.27262#S3.SS3.p1.1)\.
- Gliwaet al\.\(2019\)B\. Gliwa, I\. Mochol, M\. Biesek, and A\. WawerSAMSum corpus: a human\-annotated dialogue dataset for abstractive summarization\.InProceedings of the 2nd Workshop on New Frontiers in Summarization,Cited by:[§3\.2\.1](https://arxiv.org/html/2609.27262#S3.SS2.SSS1.p3.1)\.
- Goodfellowet al\.\(2013\)I\. J\. Goodfellow, M\. Mirza, D\. Xiao, A\. Courville, and Y\. BengioAn empirical investigation of catastrophic forgetting in gradient\-based neural networks\.arXiv preprint arXiv:1312\.6211\.Cited by:[§2](https://arxiv.org/html/2609.27262#S2.p3.1)\.
- Grattafioriet al\.\(2024\)A\. Grattafioriet al\.The llama 3 herd of models\.arXiv preprint arXiv:2407\.21783\.Cited by:[§3\.3](https://arxiv.org/html/2609.27262#S3.SS3.p1.1)\.
- Huet al\.\(2022\)E\. J\. Hu, Y\. Shen, P\. Wallis, Z\. Allen\-Zhu, Y\. Li, S\. Wang, L\. Wang, and W\. ChenLoRA: low\-rank adaptation of large language models\.InInternational Conference on Learning Representations \(ICLR\),Cited by:[§1](https://arxiv.org/html/2609.27262#S1.p2.1),[§1](https://arxiv.org/html/2609.27262#S1.p3.1),[§2](https://arxiv.org/html/2609.27262#S2.p1.1)\.
- Huanget al\.\(2024\)C\. Huang, Q\. Liu, B\. Y\. Lin, T\. Pang, C\. Du, and M\. LinLoraHub: efficient cross\-task generalization via dynamic LoRA composition\.InConference on Language Modeling \(COLM\),Cited by:[§2](https://arxiv.org/html/2609.27262#S2.p4.1)\.
- Ilharcoet al\.\(2023\)G\. Ilharco, M\. T\. Ribeiro, M\. Wortsman, S\. Gururangan, L\. Schmidt, H\. Hajishirzi, and A\. FarhadiEditing models with task arithmetic\.InInternational Conference on Learning Representations \(ICLR\),Cited by:[§1](https://arxiv.org/html/2609.27262#S1.p2.1),[§2](https://arxiv.org/html/2609.27262#S2.p4.1)\.
- Jianget al\.\(2023\)A\. Q\. Jiang, A\. Sablayrolles, A\. Mensch, C\. Bamford, D\. S\. Chaplot, D\. de las Casas, F\. Bressand, G\. Lengyel, G\. Lample, L\. Saulnier,et al\.Mistral 7b\.arXiv preprint arXiv:2310\.06825\.Cited by:[§3\.3](https://arxiv.org/html/2609.27262#S3.SS3.p1.1)\.
- Kwonet al\.\(2023\)W\. Kwon, Z\. Li, S\. Zhuang, Y\. Sheng, L\. Zheng, C\. H\. Yu, J\. E\. Gonzalez, H\. Zhang, and I\. StoicaEfficient memory management for large language model serving with PagedAttention\.InProceedings of the 29th Symposium on Operating Systems Principles \(SOSP\),Cited by:[Appendix C](https://arxiv.org/html/2609.27262#A3.p2.1)\.
- Laskaret al\.\(2023\)M\. T\. R\. Laskar, X\. Fu, C\. Chen, and S\. B\. TnBuilding real\-world meeting summarization systems using large language models: a practical perspective\.InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track,pp\. 343–352\.Cited by:[§1](https://arxiv.org/html/2609.27262#S1.p1.1),[§3\.2\.1](https://arxiv.org/html/2609.27262#S3.SS2.SSS1.p3.1)\.
- Laskaret al\.\(2024\)M\. T\. R\. Laskar, E\. Khasanova, X\. Fu, C\. Chen, and S\. B\. TnQuery\-opt: optimizing inference of large language models via multi\-query instructions in meeting summarization\.InProceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track,pp\. 1140–1151\.Cited by:[§3\.2\.1](https://arxiv.org/html/2609.27262#S3.SS2.SSS1.p3.1)\.
- Laskaret al\.\(2025\)M\. T\. R\. Laskar, J\. B\. Tremblay, X\. Fu, C\. Chen, and S\. B\. TnAI knowledge assist: an automated approach for the creation of knowledge bases for conversational ai agents\.InProceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track,pp\. 1856–1866\.Cited by:[§1](https://arxiv.org/html/2609.27262#S1.p1.1)\.
- Lin \(2004\)C\. LinROUGE: a package for automatic evaluation of summaries\.InText Summarization Branches Out,pp\. 74–81\.Cited by:[§4](https://arxiv.org/html/2609.27262#S4.p1.1)\.
- Loshchilov and Hutter \(2019\)I\. Loshchilov and F\. HutterDecoupled weight decay regularization\.InInternational Conference on Learning Representations \(ICLR\),Cited by:[Appendix C](https://arxiv.org/html/2609.27262#A3.p1.1)\.
- Onget al\.\(2025\)I\. Ong, A\. Almahairi, V\. Wu, W\. Chiang, T\. Wu, J\. E\. Gonzalez, M\. W\. Kadous, and I\. StoicaRouteLLM: learning to route llms from preference data\.InThe Thirteenth International Conference on Learning Representations,Cited by:[§1](https://arxiv.org/html/2609.27262#S1.p2.1)\.
- Ouyanget al\.\(2022\)L\. Ouyang, J\. Wu, X\. Jiang, D\. Almeida, C\. Wainwright, P\. Mishkin, C\. Zhang, S\. Agarwal, K\. Slama, A\. Ray,et al\.Training language models to follow instructions with human feedback\.Advances in neural information processing systems35,pp\. 27730–27744\.Cited by:[§2](https://arxiv.org/html/2609.27262#S2.p2.1)\.
- Parisiet al\.\(2019\)G\. I\. Parisi, R\. Kemker, J\. L\. Part, C\. Kanan, and S\. WermterContinual lifelong learning with neural networks: a review\.Neural Networks113,pp\. 54–71\.Cited by:[§1](https://arxiv.org/html/2609.27262#S1.p3.1),[§2](https://arxiv.org/html/2609.27262#S2.p3.1)\.
- Pfeifferet al\.\(2021\)J\. Pfeiffer, A\. Kamath, A\. Rücklé, K\. Cho, and I\. GurevychAdapterFusion: non\-destructive task composition for transfer learning\.InProceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics \(EACL\),Cited by:[§1](https://arxiv.org/html/2609.27262#S1.p3.1),[§2](https://arxiv.org/html/2609.27262#S2.p4.1)\.
- Raffelet al\.\(2020\)C\. Raffel, N\. Shazeer, A\. Roberts, K\. Lee, S\. Narang, M\. Matena, Y\. Zhou, W\. Li, and P\. J\. LiuExploring the limits of transfer learning with a unified text\-to\-text transformer\.Journal of Machine Learning Research21\(140\),pp\. 1–67\.Cited by:[§1](https://arxiv.org/html/2609.27262#S1.p3.1),[§2](https://arxiv.org/html/2609.27262#S2.p1.1),[§2](https://arxiv.org/html/2609.27262#S2.p2.1)\.
- Rajbhandariet al\.\(2020\)S\. Rajbhandari, J\. Rasley, O\. Ruwase, and Y\. HeZeRO: memory optimizations toward training trillion parameter models\.InProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis \(SC\),Cited by:[Appendix C](https://arxiv.org/html/2609.27262#A3.p1.1)\.
- Rosset al\.\(2025\)H\. Ross, A\. S\. Mahabaleshwarkar, and Y\. SuharaWhen2Call: when \(not\) to call tools\.InProceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies \(Volume 1: Long Papers\),pp\. 3391–3409\.Cited by:[§3\.2\.1](https://arxiv.org/html/2609.27262#S3.SS2.SSS1.p5.1)\.
- Sanhet al\.\(2022\)V\. Sanh, A\. Webson, C\. Raffel, S\. Bach, L\. Sutawika, Z\. Alyafeai, A\. Chaffin, A\. Stiegler, A\. Raja, M\. Dey,et al\.Multitask prompted training enables zero\-shot task generalization\.InInternational Conference on Learning Representations,Cited by:[§1](https://arxiv.org/html/2609.27262#S1.p3.1),[§2](https://arxiv.org/html/2609.27262#S2.p2.1)\.
- Wanget al\.\(2024\)Y\. Wang, S\. Si, D\. Li, M\. Lukasik, F\. Yu, C\. Hsieh, I\. Dhillon, and S\. KumarTwo\-stage llm fine\-tuning with less specialization and more generalization\.InInternational Conference on Learning Representations,Vol\.2024,pp\. 20380–20398\.Cited by:[§2](https://arxiv.org/html/2609.27262#S2.p2.1)\.
- Weiet al\.\(2022\)J\. Wei, M\. Bosma, V\. Y\. Zhao, K\. Guu, A\. W\. Yu, B\. Lester, N\. Du, A\. M\. Dai, and Q\. V\. LeFinetuned language models are zero\-shot learners\.InInternational Conference on Learning Representations \(ICLR\),Cited by:[§2](https://arxiv.org/html/2609.27262#S2.p2.1)\.
- Wolfet al\.\(2019\)T\. Wolf, L\. Debut, V\. Sanh, J\. Chaumond, C\. Delangue, A\. Moi, P\. Cistac, T\. Rault, R\. Louf, M\. Funtowicz,et al\.Huggingface’s transformers: state\-of\-the\-art natural language processing\.arXiv preprint arXiv:1910\.03771\.Cited by:[§3\.3](https://arxiv.org/html/2609.27262#S3.SS3.p1.1)\.
- Wortsmanet al\.\(2022\)M\. Wortsman, G\. Ilharco, S\. Y\. Gadre, R\. Roelofs, R\. Gontijo\-Lopes, A\. S\. Morcos, H\. Namkoong, A\. Farhadi, Y\. Carmon, S\. Kornblith,et al\.Model soups: averaging weights of multiple fine\-tuned models improves accuracy without increasing inference time\.InInternational conference on machine learning,pp\. 23965–23998\.Cited by:[Appendix D](https://arxiv.org/html/2609.27262#A4.p2.1),[§1](https://arxiv.org/html/2609.27262#S1.p2.1),[§1](https://arxiv.org/html/2609.27262#S1.p3.1),[§2](https://arxiv.org/html/2609.27262#S2.p4.1)\.
- Xuet al\.\(2026\)L\. Xu, H\. Xie, S\. J\. Qin, X\. Tao, and F\. L\. WangParameter\-efficient fine\-tuning methods for pretrained language models: a critical review and assessment\.IEEE Transactions on Pattern Analysis and Machine Intelligence\.Cited by:[§2](https://arxiv.org/html/2609.27262#S2.p1.1)\.
- Yadavet al\.\(2023\)P\. Yadav, D\. Tam, L\. Choshen, C\. Raffel, and M\. BansalTIES\-merging: resolving interference when merging models\.InAdvances in Neural Information Processing Systems \(NeurIPS\),Cited by:[§1](https://arxiv.org/html/2609.27262#S1.p3.1),[§2](https://arxiv.org/html/2609.27262#S2.p4.1),[§3\.1](https://arxiv.org/html/2609.27262#S3.SS1.p5.1)\.
- Yanget al\.\(2025\)A\. Yang, A\. Li, B\. Yang, B\. Zhang, B\. Hui, B\. Zheng, B\. Yu, C\. Gao, C\. Huang, C\. Lv,et al\.Qwen3 technical report\.arXiv preprint arXiv:2505\.09388\.Cited by:[§3\.3](https://arxiv.org/html/2609.27262#S3.SS3.p1.1)\.
- Yuet al\.\(2024\)L\. Yu, B\. Yu, H\. Yu, F\. Huang, and Y\. LiLanguage models are super mario: absorbing abilities from homologous models as a free lunch\.InInternational Conference on Machine Learning \(ICML\),Cited by:[§1](https://arxiv.org/html/2609.27262#S1.p2.1)\.
- Zhanget al\.\(2020\)T\. Zhang, V\. Kishore, F\. Wu, K\. Q\. Weinberger, and Y\. ArtziBERTScore: evaluating text generation with BERT\.InInternational Conference on Learning Representations \(ICLR\),Cited by:[§4\.5](https://arxiv.org/html/2609.27262#S4.SS5.p1.1),[§4](https://arxiv.org/html/2609.27262#S4.p1.1)\.
- Zhanget al\.\(2025\)Z\. Zhang, M\. Yang, Z\. Chen, Y\. Zhuang, S\. Pi, Q\. Liu, R\. Maragoud, V\. Nguyen, and A\. BeniwalREIC: rag\-enhanced intent classification at scale\.InProceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track,pp\. 1072–1080\.Cited by:[§1](https://arxiv.org/html/2609.27262#S1.p1.1)\.
- Zhenget al\.\(2024\)Y\. Zheng, R\. Zhang, J\. Zhang, Y\. Ye, Z\. Luo, Z\. Feng, and Y\. MaLlamaFactory: unified efficient fine\-tuning of 100\+ language models\.InProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics \(Volume 3: System Demonstrations\),Cited by:[Appendix C](https://arxiv.org/html/2609.27262#A3.p1.1)\.

## Appendix ATask Prompts

We list the instruction templates used for each task\. Placeholders in square brackets \(e\.g\.,\[Categories\],\[Transcript\]\) and braces \(e\.g\.,\{Length Type\}\) denote task\-specific content that is filled in at runtime\.

### A\.1Public Suite

Intent Classification``` Classify the utterance intent. Return JSON with keys: intent, domain, language, source_dataset. # zero-shot condition only: The "intent" MUST be exactly one of these 27 labels: [27 intents] The "domain" MUST be exactly one of these 11 labels: [11 domains] The "language" is the ISO 639-1 code of the utterance (e.g. en). Dataset: [Dataset] Utterance: [Utterance] ```

Question Answering``` Answer the question using only the retrieved content. Return JSON with keys: answer, evidence, article_ids. [Retrieved Content] [Question] ```

Dialogue Summarization``` Summarize the conversation or meeting. Return JSON with keys: summary, source_dataset. [Conversation] ```

Chatbot Response``` Choose the correct tool-use decision for the user request. Return JSON with keys: decision, answer, source_dataset. Tools: [Tool Specifications] [User Query] ```

### A\.2Internal Suite

Each internal prompt follows the format below, where the task\-specific instruction is placed in the\[Prompt\]field and the call transcript in the\[Transcript\]field:

Internal prompt format``` [Prompt] # Transcript Start # [Transcript] # Transcript End # [Response] ```

Action Items``` Generate a newline separated list of work, business or service related TODO tasks that are still not done at the end of the conversation and should be completed after the conversation. [Transcript] ```

Call Outcome``` For the following conversation transcript, select the best category from the list provided below to describe the outcome of the conversation. Respond with "Other" if no category applies. Do not respond with any words other than the category. The categories: [List of Categories]. ```

The call\-outcome label space is fixed at 30 categories\. Examples include: call back, unsuccessful contact, voicemail success, payment / billing, status update, scheduled appointment, cancellation\.

Meeting Summaries``` Generate a {Length Type} summary of the following conversation {Format} without assessing its quality. ```

Here,\{Length Type\}specifies the desired length \(very short, short, long, or descriptive\) and\{Format\}the desired format \(e\.g\. free text, bullet points, or an array of executive summaries\)\.

Table 6:Suite\-average results for all models\.ST−\-MTdenotes the difference between single\-task and multi\-task full fine\-tuning\.Purpose of Call``` For the conversation below, identify a single category for the purpose of the conversation chosen from this list: [List of Categories] ```

Purpose of call differs from the other label tasks in that its candidate list is supplied per example rather than fixed: prompts come in several templates, each offering a different shortlist of 8–10 categories drawn from a broader taxonomy of roughly 33 observed in the training data\. Examples include: user education, cancellation, support, status inquiry, product support, promotions, account support, maintenance\. Because the offered list varies by item, we exclude this task from the output\-format\-validity analysis in the paper\.

## Appendix BFull Model Coverage

The main table reports the seven models that span the size and family axes most directly\. Table[6](https://arxiv.org/html/2609.27262#A1.T6)gives the internal\- and public\-suite averages for all thirteen bases, together with each base’s zero\-shot competence on the same suite\.

## Appendix CTraining and Inference Parameters

All training experiments were run across 56 H200 GPUs\. The training pipeline is implemented using LLaMA\-Factory\([Zheng et al\., 2024](https://arxiv.org/html/2609.27262#bib.bib33)\)with DeepSpeed ZeRO\-2 \(≤\\leq8B\) / ZeRO\-3 \(9B\)\([Rajbhandari et al\., 2020](https://arxiv.org/html/2609.27262#bib.bib35)\)\. Based on performance in the validation set, we select the following hyperparameters for training: AdamW\([Loshchilov and Hutter, 2019](https://arxiv.org/html/2609.27262#bib.bib37)\)as the optimizer, learning rate1\.5×10−51\.5\\times 10^\{\-5\}, cosine schedule with 10% warmup, per\-device batch set to 1 with gradient checkpointing enabled but without any accumulation\. A maximum of 5 epochs was run with the context length being set to a maximum of 16K tokens\. For LoRA, we use rank 16,α=32\\alpha=32, dropout 0\.05, learning rate1×10−41\\times 10^\{\-4\}\.

For inference, we use vLLM\([Kwon et al\., 2023](https://arxiv.org/html/2609.27262#bib.bib34)\)using tensor\-parallel\-8 on 8 A100\-80GB with the following decoding parameters: temperature=0=0\.

## Appendix DModel Merging: Effect of the Interpolation Weightα\\alpha

In Section[4\.4](https://arxiv.org/html/2609.27262#S4.SS4), the merge\-with\-base results primarily use a fixed interpolation weightα=0\.5\\alpha=0\.5for consistency\. Here we examine howα\\alphatrades off in\-task specialization against off\-task robustness\. Figure[5](https://arxiv.org/html/2609.27262#A4.F5)sweepsα∈\[0,1\]\\alpha\\in\[0,1\]for the Qwen3\.5\-9Bcall outcomespecialist merged with its base \(θmerged=α​θFT\+\(1−α\)​θbase\\theta\_\{\\text\{merged\}\}=\\alpha\\,\\theta\_\{\\text\{FT\}\}\+\(1\-\\alpha\)\\,\\theta\_\{\\text\{base\}\}\), evaluating the merged model on all four internal tasks\. Atα=0\\alpha=0the merged model reduces to the base, while atα=1\\alpha=1it recovers the unmerged specialist\.

Two trends are evident\. The in\-task call\-outcome score rises sharply and saturates early: it reaches77\.877\.8byα=0\.2\\alpha=0\.2and82\.282\.2byα=0\.3\\alpha=0\.3, against84\.484\.4for the unmerged specialist, so essentially all of the in\-task benefit is bought byα≈0\.3\\alpha\\approx 0\.3\. The three off\-task scores instead stay near their zero\-shot values for smallα\\alphaand then collapse: off\-task action items fall from46\.746\.7atα=0\.2\\alpha=0\.2to3\.63\.6for the pure specialist, meeting summaries from53\.853\.8to3\.53\.5, and purpose of call degrades to near zero beyondα=0\.3\\alpha=0\.3\. Purpose of call is the most fragile, losing most of its capability betweenα=0\.2\\alpha=0\.2andα=0\.4\\alpha=0\.4\. Consequently the best trade\-off lies inα∈\[0\.15,0\.30\]\\alpha\\in\[0\.15,0\.30\], where in\-task quality is already within a few points of its maximum while off\-task skills are largely retained\. We keepα=0\.5\\alpha=0\.5in the main\-text tables for consistency across all merge configurations, but recommend otherα\\alphavalues in practice depending on in\-task vs off\-task priority\. This behavior is consistent with prior observations on linear mode connectivity\([Frankle et al\., 2020](https://arxiv.org/html/2609.27262#bib.bib27)\)and model soups\([Wortsman et al\., 2022](https://arxiv.org/html/2609.27262#bib.bib8)\): a fine\-tuned model and its base lie in the same low\-loss region of the parameter space, so points interpolated between them remain strong models\.

Figure 5:Effect of the interpolation weightα\\alphawhen merging the Qwen3\.5\-9Bcall outcomespecialist with its base model, evaluated on all four internal tasks\.
## Appendix ETraining and Serving Cost

Table[7](https://arxiv.org/html/2609.27262#A5.T7)reports measured training and serving costs\. Each training value represents one five\-epoch MT\-Full run over all four tasks\. Because four ST\-Full runs process the same total data, MT\-Full does not reduce token\-level training compute, while its advantage is operational, requiring one training job, checkpoint, endpoint, and no router\. At a fixed model size, adaptation strategies have similar per\-request costs during inference, but deployingNNspecialists multiplies the serving need byNN\.

Table 7:Measured wall\-clock time for one five\-epoch MT\-Full run over four tasks using eight H200 GPUs\.