MASTER: Enhancing Large Language Model via Multi-Agent Simulated Teaching
Authors: Liang Yue
Yihong Tang
Kehai Chen
Jie Liu
Min Zhang
Venue: NeurIPS 2025 poster ()
Keywords: Instruction Fine-Tuning；Data Augmentation；Multi-Agent Systems；Natural Language Processing
Primary area: deep_learning
TL;DR: We introduce MASTER, a multi-agent framework that enhances LLM instruction data through simulated pedagogical interactions, significantly improving reasoning and generalization.

Abstract: Instruction fine-tuning is crucial in NLP tasks, enhancing pretrained models' instruction-following capabilities and task-specific performance. However, obtaining high-quality fine-tuning data for large models is challenging due to data collection difficulties and high production costs. To address this, we propose MASTER, a novel data augmentation method that enriches original data through interactions among multiple agents with varying cognitive levels. We simulate three pedagogically grounded teaching scenarios, leveraging multi-agent conversations to generate high-quality teacher-student interaction data. Utilizing MASTER, we construct BOOST-QA, a fine-tuning dataset augmented from existing datasets like Orca-Math-200k, ProcQA, and OpenHermes2.5. Experiments show that models fine-tuned with BOOST-QA perform excellently across multiple benchmarks, demonstrating strong multitask generalization. Notably, MASTER significantly improves models' reasoning abilities in complex tasks, providing valuable insights for future research.

MASTER: Enhancing Large Language Model via Multi-Agent Simulated Teaching
Liang Yue'*, Yihong Tang Kehai Chen li, Jie Liu", Min Zhang'
Harbin Institute of Technology, Shenzhen, China Shenzhen Loop Area Institute (SLAI), Shenzhen, China 3 Harbin Institute of Technology. China (2201109218ss hit edu. en, chenkehai @hit Chittedde edu. cn}
Abstract
Instruction fine-tuning is crucial in NLP tasks, enhancing pretrained models' instruction-followiin capabilities and task-specific performance. However, ob- taining high-quality fine-tuning data for large models is challenging due to data collection difficulties and high production costs. To address this, we propose MASTER, novel data augmentation method that enriches original data through interactions among multiple agents with varying cognitive levels. We simulate three pedagogically grounded teaching scenarios, leveraging multi-agent versations to generate high-quality teacher-student interaction data. Utilizing MASTER, we construct BOOST-QA, fine-tuning dataset augmented from ex- isting datasets like Orca-Math-222 200k, ProcQA, and OpenHermme2.55, Experiments show that models fine-tuned with BOOST-QA perform excellently across multi- ple benchmarks, demonstrating strong multitask generalization. Notably, MAS- TER significantly improves models' reasoning abilities in complex tasks, pro- viding valuable insights for future research. Our code is publicly available at https: thub. Saam/Toohoo/MMATTE
Introduction
In recent years, instruction-tunnin -tuning post-training has become of the cornerstones of large language models (LLMs) 2 To meet the growing demand for data, data synthesis has been widely studied. For example, Yoo al. combined subsets training examples and embedded them into model prompts to generate new, high-quality textual instances [3], demonstrating notable performance improvements in text classification tasks. Ding al. and Xu et al. proposed self-chat approaches based on predefined prompt templates and dialogue seeds, using large language models (LLMs) to For example, Yoo al. combined subsets of training examples and embedded them into model prompts to generate new, high-qualin textual instances (3) demonstrating notable performance improvements in text classification tasks. While these methods enhance the diversity of synthetic instruction data, they rely heavily manually crafted prompts and dialogue seeds and often lack clear interaction mechanisms, ultimately leading to mismatch between the generated data and real-world instruction scenarios.
generate diverse instruction data through self-dialogue 4 5
To address these challenges, our study introduces novel multi-agent interaction framework aimed enhancing original problem-solving datasets within simulated educational scenarios. Specifically, we developed a system comprising teacher and student agents. Through their collaborative interactions. any existing instruction dataset can be transformed into Multi-Agent Simulated Teaching Enhanced
'qqual contribution. CCoreesponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Resource (MASTER) framework. MASTER simulates three educational scenarios enror correction, collaborative debate, and analogical reasoning- leveraging distinct conversational protocols and model prompts, ultimately resulting in the creation of a high-quality instruction dataset named BOOST-QA (Behaviorally Oriented Overlay of Simulated Teaching for QA).
Using the large-scale, high-quality instruction dataset BOOST-QA generated through the MASTER framework, we fine-tuned several mainstream base models. To rigorously assess the effectiveness of the MASTER method, we conducted comprehensive experiments comparing the performance of base models fine-tuned on the original datasets, datasets augmented by other methods, and those created through MASTER. The results show that BOOST-QA -AQQA significantly enhances the diverse capabilities of large language models (LLMs), outperformigg several existing approaches focused on data augmentation and selection.
Our main contributions summarized as follows:
We introduce the application of multi-age agent simulated instructional scenarios in post-training data synthesis and propose a novel data augmentation method, MASTER.
By applying MASTER to portions of Orea-math-22 200k, ProcQA, and OpenHermes2.5, we construct an efficient instruction fine-tuning dataset, BOOST-QA.
We design comprehensive experiments to assess our MASTER method. Notably, in bench- mark tests, multiple models fine-tuned with 19K instruction-respone pairs from our BOOST- QA exhibit significant performance improvements across various task domains.
Relate Work
2.1
Data Synthesis and Augmentation
In recent years, data synthesis and augmentation techniques have become essential for enhancing LLM performance and generalization capabilities [6] 77. Early approaches focused primarily on simple lexical and positional transformations or employed LLMs to generate new training samples through synonym substitution of sentences from original datasets [8 While these methods partially addressed data scarcity, they risked introducing noise-induced semantic drift and often produced insufficient sample diversity for complex tasks [1I] To overcome these limitations, prompt- guided LLM approaches for comprehensive data expansion have emerged as a promising alternative, including methods such constraint augmeette problem evolution to deepen original questions [12), multi-task contextual generation by sampling from seed pools [13], and knowledge- tree recursive QA to extend initial keywords [14]. These techniques improve content diversity and generalizability of synthetic instructions, yet remain overly reliant on predefined prompts and keywords while lacking authentic natural language contexts. In contrast prior work, our approach enhances problem-solving data quality by simulating real classroom learning scenarios through the incorporation of authentic educational events, achieving superior generalizability and ecological validity.
2.2 Multi-Agent Simulation of Human Interaction
In recent years, agentic systems based large language models have become research focus [15 16). Building on this trend, multi-agent simulation of human interaction has demonstrated sigmificant potential in tasks such as personality analysis and social behavior research 117 Early multi-agent systems primarily focused on two problem categories: goal-aligned collaborative tasks and game-theoretic competitive scenarios 22 Moreover, portion of the work directed toward improving the agents" ability to engage in role-play (3 Recent advancements have substantially expanded agent populations to investigate social dynamics, exemplified by Mou al.'s work employing agent swarms to model opinion propagation in social networks (26) and Stanford's "Virtual Town" simulating complex human behavioral patterns through agent socialization (27). However, practical applications leveraging multi-agent interaction for real-world problem-solving remain limited. Our proposed school-agent framework is specifically designed for synthesizing diverse, high-quality data to address this gap and meet operational requirements.

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2.3 Knowledge Distillation
Knowledge distillation enables the transfer of knowledge from large teacher models to compact student models while preserving performance and reducing computational complexity. The seminal work by Hinton et al. first introduced this concept, demonstrating how soft labels could effectively compress models by distilling integrated knowledge from larger architectures [28]. Subsequent advancements by Jing et al. incorporated conditional generative adversarial networks to refine student logits outputs through adversarial training, achieving closer alignment with teacher outputs (29). Zhang et al. expanded the paradigm through mutual learning among multiple student models, proving effective for collaborative training scenarios [30]. Novel directions emerged through Tung/Park
et al.'s focus on relational similarity between teacher-student networks [31 (2). while Xu et al.'s
alternating sampling method significantly narrowed performance gaps in complex mathematical reasoning tasks [33] The recent "Branch-Merge Distillation" by DeepSeek successfully transferred DeepSeck- RR''s capabilittes Qwen models across STEM benchmarks [34]. Our approach diverges by employing agdnt-mediated interaction to inject novel cognitive patterns into raw data, funda- mentally overcoming knowledge distillation's 's inherent limitations in generalization capacity and teacher-dependent behavioral constraints through enhanced data format learnability.
3
Methodology
3.1
Overview
This section introduces our multi-agent classroom simulator, MACLASS. As illustrated in the Figure I MACLASS enables LLMs to realistically play the roles of teachers and students through carefully designed prompts and uses a set of original question-answer data as input to generate simulated teaching interaction scenarios. MACLASS integrates diverse real-world educational settings and adheres the design principle of embedding authentic and effective pedagogical methods into the multi-agent interaction process. Our approach primarily addresses the following two challenges: (I)
How can effective educational principles be incorporated into agent-based teaching processes? (2)
How can we ensure coherent and natural interactions among agents across different scenarios?
To address the first challenge, we integrate established pedagogical principles into classroom interac- tions through three key approaches: teachers guide students correct mistakes and solve problems independently. facilitating experiential learning from errors [35; teachers facilitate group debates to enhance critical thinking and analytical skills (36); and teachers encourage analogical reasoning. enabling students to develop inductive learning abilities by solving structurally similar problems [37]. The above methods are each developed into distinct instructional scenarios, featuring multi-turn interactions among multiple agents. The utterances from these agents are then concatenated and organized into ShareGPT-forma data, effectively integrating foundational educational principles with the reasoning capabilities of intelligent agents within the dataset.
To address the second issue, we ensure that the interactions between multiple agent roles are co- herently and logically controlled. To this end, we design precise interaction management rules that govern the speaking order of agents across different teaching scenarios, and assign scenario- specific prompts to each agent at different turns. Specifically, we model the dialogue process as: D = (D,,D2..... D...... Dn, : p). ,were D, denote the utterance content of an agent at the i-th turn, and let p represent the prompt used by the agent at the current teaching step. Under this framework. the predefined speaking order and prompt assignment strategy ensure the coherence of the dialogue flow, effectively achieving the intended pedagogical goals.
By adopting this method, we implement a fluent and well-controlled aueti- classroom inter- action module that successfully simulates authentic and effective teaching processes. The detailed configuration provided in the Appendix.
3.2 Agent role construction
Studies have shown that interactive teaching can significantly enhance students' learning quality. However, its effective implementation relies on efficient dialogue exchanges between teachers and students, which remains a challenging task [38]. Simulating high-quality classroom interactions using multiple agents often encounters problems such as agents competing for roles, drifting off-topic,
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or redundantly repeating previous responses. To address these issues, we introduce two strictly constrained types of agents: Teacher agents and Student agents. In our framework, we assign large language models (LLMSs) with distinct prompts that enable them to assume different roles in multi- agent setting or to perform different instructional tasks within the same role definition. This approach facilitates both functional cooperation and procedural control. Formally, this can be expressed as A = (L, PR.), R2 E R = [R], B2.................. Ra...... Rn).
In our framework, each agent A powered by an LLM L is assigned role-specific prompts PR. corresponding to distinct task phases R.. For instance, student agent utilizes different prompts when initially making an error versus when correcting it. This structured prompt design ensures agents operate within their designated roles and phases, minimizing role confusion and task redundancy, thereby enhancing the quality and stability of the collaboratively generated educational dialogues.
Teacher Agents In a classroom environment, the teacher not only serves as the primary source of knowledge transmission and student guidance, but also plays pivotal role in shaping the overall learning experience, managing instructional dynamics, and fostering critical thinking. As a core component of the multi-agent classroom framework, the teacher must simultaneously fulfill multiple instructional functions, including delivering content, assessing student understanding, providing timely feedback, and adapting pedagogical strategies accommodate diverse learning needs.
Upon receiving an original question and its standard answer, the teacher agent conveys the problem details to the student, offering brief explanations facilitate understanding. When provided with a student's solution, along with the corresponding question and standard answer, the teacher agent identifies any errors in the student's response and supplements the instruction with correct problem- solving strategies, guiding the student to independently rectify previous mistakes. This structured approach ensures the quality and stability of the multi-agent educational dialogues.
Student Agents As the recipients of instructional content and the primary agents in the problem- solving process, students play a pivotal role in integrating the three pedagogical methods introduced in 3.1 into realistic educational genariossStudent agents are expected not only to respond to teacher instructions but also to revise their previous answers based on prior attempts and peer debates, articulating their own perspectives accordingly.
3.3 Class specific settings
Our work creates a classroom environment that diverges from traditional instructional paradigms. Multi-agent systems that rely solely on predefined operations are insufficient to effectively simulate concrete pedagogical strategies. To authentically integrate the pedagogical methods outlined in 3.1 into the synthesized multi-agent classroom dialogue data, we have designed and developed a Classroom Interaction Manager comprising three modules: "Error Making and Correction", "Dehate", and "Analogical Problem Retrieval and Solving". These modules respectively support the control of three distinct scenarios, illustrated in Figure [ The multi-turn question-answerigg augmented data generated from these three scenarios are concatenated in the ShareGPT format to construct a high-quality dataset named BOOST-QA.
Error Correction Module This module is designed to simulate a classroom scenario where student agent initially provides an incorrect or incomplete solution to given problem. Subsequently, teacher agent analyzes the student's response in conjunction with the standard answer to identify errors and offer correct reasoning. Finally, the student agent, leveraging the prior interaction and the standard answer, independently formulates a corrected solution. Specifically, employ the model with a temperature setting of 0.8 for the initial student response, promoting the generation of diverse and imperfect answers. For the subsequent correction phase, both teacher and student agents utilize the more capable Qwen2.5- 1-1B2-5-----111 I4B-Instruct model with a temperature of 0.2, ensuring accurate emror identification and high-quality reasoning.
In practice, training models with augmented data constructed by this module can inject structured noise into the gradient descent process. This simulates interactions across different cognitive levels, enabling the model to explore high-loss regions associated with student model errors and converge along smoother paths defined by corrected answers. Such an approach facilitates escaping local

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Oriinnal Questions
Solations
Guidinco& Correciin
Solutions
Scene 1: C.A
Dall Student Model (Mp)
Tencher Model (My)
Smart Stident Model (Mg)
Original Questions
Solutions
Solutions
(8.)
Student Model 2 (S.)
Final Focus
Scene 2:DA
Student Deminant ModellMap)
Dehote Record
Final Answer
Original
Guidince
Solution
Human:
GPT:
Teacher Model (Mp)
Student Model
Solution
Human:
GPT:
Scene
Questions matching similarity
Similar Qucstions
Student Modkl
Concatenate multi-tum interccionss into ShareGPT forar
Figure multi-agent system-basee based data augmentation pipeline that simulates three different pedagogical contexts to enhance grestion-answering datasets. From top to bottom, the scenes are Correction Augmentation, Debatement Augmentation, and Analogical Augmentation.
minima and aids in identifying and avoiding common error patterns during inference. The gradient expression is as follows:
V V VeCang (0) VoC(O) + Biz, AD [Vo (fo(z), 6y)]
In this context, L()) denotes the conventional supervised learning loss function. The parameter ? represents the perturbation intensity introduced by multi-agen interactions, influenced by factors such inference temperature. The term (r,, signifies the expectation over the augmented data distribution AD. Here, E is the per-sample loss function, fo(x= corresponds to the corrected solution generated by the 14B teacher model for the original problem æ, and by denotes the perturbed answer label produced by the 0.5B student model.
Debate Interaction Module This module is designed to construct multiple student agents, each analyzing the question and the responses of other students to express their own perspectives. This approach aims to capture diverse problem-solving strategies and enhance data diversity. We employ identical prompts build three student agents: St, S2. and S3. S3 utilizes the Qwen2.5-144 3-144B-Instrud model with temperature setting of 0.2, serving the summarizer and decision-maker in the debate. Conversely, S and S2 employ the Qwen2.5-7B-Insstee regular participants in the discussion. During the data augmentation process, S1 and S2 take turns speaking for one or two rounds, after which S3 provides summary. The output of the agent in the i-th round, denoted A,, can be represented by A, f(H1, 0,), where I/ signifies the current classroom dialogue history, and 0g denotes the output temperature corresponding to the agent.
model with a temperature of 0.6, acting as
Analogous to the error correction module, this component employs discussions among multiple agents simulate the cognitive diversity of students. By fine-tuning the model with debate-enhanced data, it facilitates the smoothing of the loss landscape, thereby guiding the optimization trajectory away from sharp local extrema and enabling the learning of diverse, high-quality problem-solving strategies. The gradient representation of this module is as follows:
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K V (ol(So()),
VoLang (
vol(fo(r).)) +
original gradient
debate gradieet
Here, B(x.y)) denotes the expectation over the augmented data distribution D. The term Vol(fo(z); y) represents the original gradient. The parameter À is the debate intensity coeffi- cient, influenced by factors such as model inference temperature and the number of debate rounds. deenotes the number of debate rounds, yi the answer provided by the agent in the current round, and (()( v represents the single-sample loss.
Similar Question Retrieval Module This module facilitates basic interactions between teacher and student agents. After the student completes the first-round response, the system retrieves similar questions based on the initial prompt. By constructing new prompts for the student to solve related problems and subsequenly performing format concatenation, the system builds analogy-basee data. This approach encourages the application of existing knowledge to analogous scenarios, enhancing knowledge transfer capability.
Specifically, small subset of questions is randomly selected from the original dataset as the first-round questions Qist, while the remaining questions form the retrieval pool. Using the all- MiniL.M-L6-v2 model, embeddings are computed, and cosine similarity employed to identify questions in the pool that closely resemble Qist- From these, one question is randomly chosen as the analogy reasoning question Qnnd for the student's s second-round response, as illustrated in the accompanying formula. Both student and teacher agents utilize the Qwen2.5- 1ioon..--44BB. 14B-Instruct model with temperature setting of 0.2. Q denotes the complete question dataset, and Qi.t represents the complement set of first-round questions, the analogy retrieval process is as follows:
Q2et Random (Top-k (cos aatsssssmm___ Si 2)) Qist U Q.
Specifically, we employ the ShareGPT format to concatenate two rounds of question- answeer dialogues from aueti- students addressing similar problems into single mixed training sample. This approach facilitates joint modeling of locally similar samples within the semantic space during model training, effectively serving as an implicit interpolation- augmentation. Moreover, this mechanism encourages the model to focus subtle differences between similar samples, thereby learning the core semantics of the task and enhancing robustness. The loss function for training the model with this augmented data is defined as follows:
Cmix - log x2)
Here, 2y and 22 denote the original problem texts, while y1 and 3/2 represent the corresponding original solution texts. The formula thereby signifies the process of learning the joint probability distribution between similarly structured and closely related sample pairs.
Experiments
4.1
Experimental Setup
Training datasets. We used three instruction-tuning datasets: (1) Sooe-Matt--ood-200000 high- quality set of elementary math QA pairs generated via multi-agent collaboration [3)] (2) ProcQA mixed-modality programming QA pairs from StackOverflow 400; and (3) OpenHermes 2.5, general-purpose dataset covering commonsense QA and reasoning. We sampled 10,000 instances each from Brea-Math-Wood-222 Word-200K and ProcQA, and 9,000 from OpenHermes 2.5, forming the original dataset (ori-data). Applying the MASTER augmentation method to ori-data produced an equal-sized enhanced dataset (19,000 samples), termed BOOST-QA. Correctness verification with locally deployed Qwen2.5-32B. Instruct model showed only 4.1 % of augmented samples contained procedural reasoning errors.

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Evaluation datasets. We evaluated our method on HumanEval 41,, MBPP 42), MATH [3].
MMLU-PRO-MATE 44. MMLU 45], ARC [46] and SCI-Q These datasets encompass various domains and task types, including human-written coding challenges, mathematical problem-solving. multi-choice questions, and scientific reasoning. thereby providing comprehensive assessment of our method's s capabilities. During evaluation, we assessed the zero-shot capabilities of the MASTER- model series across these datasets. The inference temperature was set to for HumanEval and MBPP, and to 0.2 for all other datasets.
Models for data augmentation and training. We employed the Qwen2.5- Instrruuttt series of models 48 as the foundational models to enhance the original data through multi-agen classroom inter- actions; specific configurations are detailed in 3.3 To evaluate the effectiveness of MASTER, we utilized three base models: LLaMA-3-77 1--MA-3-7B-bar b-base [49], Owen2.5-7B-baal [48], and Histral-7B-bass
[0) By fine-tuning these models on BOOST-QA, ori-data, and other high-quality datasets con- structed using alternative methods, and subsequently comparing their performances, we validated the superiority the MASTER method in enhancing data quality.
4.2 Baselines
We selected four baseline methods for comparison with MASTER. First, we employed traditional text augmentation techniques by injecting character-level noise into the original text. Inspired by EDA, RandomAug and SpellingAug [5]] were selected and are both open-sourced in the GitHub
project nlpaug.
The third baseline is TAGCOS [52], which computes gradient representations for each sample in the original dataset, clusters similar data points, and then applies a greedy algorithm within each cluster to effectively select high-quality data subsets for instruction fine-tuning. This approach emphasizes efficiency and relevance in data selection, aiming to reduce redundancy while preserving diversity in the fine-tuning corpus.
The final baseline is CoT Collection, proposed by Seungone Kim et al., which aims to enhance the reasoning capabilities of small and medium- sized language models in zero-shot and few-shot tasks through chain-of ciann-ffthouggg thought (CoTT) fine-tuning [53]. It aggregates a large and diverse set of Co'T- annotated samples from multiple sources to provide explicit reasoning supervision, thereby helping models better learn intermediate reasoning steps.
To ensure fair comparison, used the TAGCOS method select high-quality subsets of 5K, 5K, and 9K samples from the original training datasets Orca-Math-200K. ProcQA, and OpenHermes2.55, respectively, and combined them into new high-quality training set. RandomAug, Spelling CoT Collection, and our MASTER were then applied to randomly augment an equal amount of original data. Each method was used to fine-tune the pretrained model using LoRA for epochs with a learning rate of le-4.
4.3
Main Results
We present the primary results of different models trained on BOOST-QA and Ori-Data across various benchmarks Table T and Table 2 and compare them with multiple baseline methods in Table 3 We use accuracy as the evaluation metric for mathematics and general tasks. For objective questions. answers are extracted reliably using regular expressions, while subjective questions are evaluated for correctness by the QwBB Insssrruttt model based on the corresponding reference answers. For programming tasks such as HumanEval and MBPP, we adopt Pass as the primary evaluation metric. Our findings are follows:
BOOST-QA has demonstrated performance improvements across various models. In the ex- perimental results presented in Table Table all baseline models fine-tuned with the augmented dataset BOOST-QA generally outperformed those fine-tuned with the original, unaugmented dataset Ori-Data across multiple benchmark tests. This indicates that our data augmentation method effec- tively enhances the learnability of the original data, thereby improving the model's generalization ability for the tasks.
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The BOOST-QA dataset enabled models to achieve better performance compared to other baselines. the experimental results presented in Table 3 the LLaMA3-8B-bal model fine- tuned with BOOST-QA dataset consistently outperforms wide range of baseline methods across multiple benchmarks in mathematics, programming. and general tasks. This highlights the significant advantages of our MASTER data augmentation method.
Table I: Performance comparison of models fine-tuned with Ori-Data and BOOST-QA (Part I).
Model
MATH
MMLU-PRO-MATH
MBPP
HumanEval
Ori
BOOST-QA
Ori
BOOST-QA
Ori
BOOST-QA
Ori
BOOST-QA
LLaMA3-8B
21.58
23,90
13.55
27.39
65.30
67.20
39.02
50.61
Qwen2.5-7B
71.00
70.54
24.35
44.41
78.00
79.10
22.56
42.07
Mistral-7B
15.74
17.58
7.18
13.92
56.30
55.00
17.68
28.05
Table 2: Performance comparison of models fine-tuned with Ori-Data and BOOST-QA (Part 2).
Model
MMLU
ARC
SCI-Q
AVERAGE
Ori
BOOST-QA
Ori
BOOST-QA
Oni
BOOST-QA
Ori
BOOST-QA
LLaMA3-8B
48.13
48.13
57.76
61.52
76.50
80.10
45.98
51.26
Qwen2.5-7B
24.05
50.12
20.90
68.52
20.00
69.70
37.27
60.64
Mistral-7B
27.59
35.89
32.25
47.70
70.50
49.10
32.46
35.32
Table 3: Performance comparison of models fine-tuned with BOOST-QA and other baselines.
Method
Ori RandomAug
SpellingAug
TAGCOS
CoT-fine
BOOST-QA
MATH
21.58
21.16
20.86
26.50
21.02
23.90
MMLU-PRO-MATII
13.55
14.58
13.40
12.95
14.36
27.39
MBPP
65.30
61.90
63.50
61.40
61.40
67.20
HumanEval
39.02
31.10
37.20
42.68
33.54
50.61
MMLU
48.13
38.35
24.21
46.91
41.08
48.13
ARC
57.76
41.98
22.61
61.09
47.87
61.52
SCI-Q
76.50
62,30
22.70
84.00
68.50
80.10
Average
45.98
38.77
29.21
47.93
41.11
51.26
The BOOST-QA dataset significantly enhances the model's capability in solving complex multiple-choice questions. Notably, We found that our MASTER series models achieved remark- able improvements multiple-choice question tasks. Upon observing this phenomenon, incorpo- rated additional multiple-choice benchmark tests, with the results illustrated in Figure Across the current eight test datasets, the MASTER method demonstrated consistent improvements exceeding 5% compared to training with the original data, peaking at a 31.46% enhancement. Through analysis found that the MASTER- models generated significantly
of the models' inference outputs, longer reasoning chains than those trained on the original data, where outputs tended to be shorter or limited direct option selection. This indicates that our BOOST-QA dataset effectively internalizes the models' reasoning capabilities, enabling better generalization when addressing complex problems.
4.4 Ablation Study
In this section, we meticulously design ablation experiments by fine-tuning the LLaMA3-8B-base model using training data constructed from various combinations educational scenarios. This approach is intended to assess the impact of different teaching scenarios model performance.
Effects of two scenarios In a subsequent series of ablation experiments, as detailed Table 4 we explored the impact of combining different pairs of educational scenarios on model performance. Specifically, ME&DB (Make Error Debate), ME&EP (Make Error and Expand), and DB&EP (Debate and Expand) denote models fine-tuned with augmented data that integrates these scenario pairs. To construct these dual-scenario datasets, we meticulously removed data lacking the corre- sponding scenarios from BOOST-QA and supplemented it with carefully selected samples from

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Performance Comparison Math Benchmarks
Original
MASTER
431.46
50
Accuracies (%6
+-2047
40
+13.55
+11.81
+1447
+2.32
20
+10.53
10
MATHQA
TAL-SCOSK
MATH-MC
CSMBIMC
PITHONIO-MCC
CODEMMILL-CC
CODEMMILU-AS
CODEMMLU-CR
Figure 2: Results from the complex multiple-choice question test show maximum improvement of 31 .6% and an average improvement of 15.50% CODEMMLU-CC, CODEMMLU-AF, and CODEMMLU-CR are abbreviations for SODEMMLU-CODD-COMPLLN CODEMMLU-API FRAMEWORKS, and CODEMMLL-CODD-RRPAARR respectively.
Table Performance comparison of models fine-tuned with partial and full multi-agent simulated teaching scenarios across multiple benchmarks. ME, DB, and EP are the abbreviations for Make Error, Debate, and Expand, respectively.
Method
Ori
ME&DB
ME&EP
DB&EP
ME
DB
EP
Full
MATH
21.58
19.88
19.90
23.48
17.96
21.90
22.40
23.90
HMLU-PRO-MATH
13.55
16.14
17.25
17.62
13.40
11.32
10.58
27.39
MBPP
65.30
56.10
52.90
66.10
52.90
63.50
61.60
67.20
HumanEval
39.02
31.70
36.59
45.73
23.78
47.56
44.51
50.61
MMLU
48.13
40.08
35.36
32.64
40.54
19.86
31.40
48.13
ARC
57.76
49.57
41.13
32.34
47.35
50.26
38.14
61.52
SCI-Q
76.50
56.30
63,60
55.20
67.90
54.40
50.20
80.10
Average
45.98
38.54
38.10
39.02
37.69
38.40
36.98
51.26
Ori-Daz Data. The experimental results indicate that augmenting with only one or two scenarios fails to significantly enhance model performance. In contrast, the MASTER model, which is trained with data augmented from all three educational scenarios, consistently outperforms the model trained on the original data across all test sets. This finding underscores the complementary and indispensable roles of each educational scenario in the data augmentation process, highlighting that a combination of multiple scenarios is essential for optimal model performance.
Effects of one scenarios In Table we systematically evaluated the performance of models trained datasets augmented using only single educational scenario-sppecffcally ME (Make Error), DB (Debate), or EP (Expand). The results consistently showed that these models underperformed compared the model trained solely on the original dataset across all test sets. This outcome highlights the limitations of augmenting data with a single scenario, as it fails to provide the comprehensive learning experiences necessary for robust model performance. Furthermore, the models trained with only one scenario exhibited a lack of robustness when faced with diverse test conditions, indicating that singular approach is insufficient for comprehensive learning. Our findings suggest that integrating multiple educational scenarios is crucial for enhancing the adaptability and generalizability of the models, as each scenario contributes unique learning signals that collectively improve model performance. Therefore, we conclude that combination of varied educational scenarios is essential for effectively improving model performance, as it provides a more holistic and diverse learning environment.

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Conclusion
This study systematically investigates the impact of constructing multi-agent instructional scenarios on question-answent (QA) data augmentation. To obtain high-quality instruction fine-tuning data, we simulate three distinct educational scenarios using multiple agents, introducing varying levels of cognitive interaction into the original data. This approach aims to enhance the convergence efficiency of the base model on the augmented data. Furthermore, during inference phase, we implement an error-correction interaction pattern that mirrors the structure of the training data, ensuring consistency between training and reasoning processes. Our experimental results validate the effectiveness of this comprchensive framework in improving model performance.
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Acknowledgments and Disclosure of Funding
We thank the reviewers for their constructive comments. This work was supported in part by the National Natural Science Foundation of China under Grant 623507 10797, Grant 62276077, Grant 62406091, I, and Grant U23B2055, in part by CCF-Baidu Open Fund (CCF-Baidu 202404), in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2024A151501 1205, and in part by the Shenzhen Science and Technology Program under Grant KQID2024402910215466 and Grant ZDSYSS202222222290000000
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Appendices
A
Author Contributions
Liang Yue designed and implemented a detailed multi-agent interaction process, conducted extensive experiments, refined the training data augmentation, supervised fine-tuning (SFT), and evaluation procedures, and successfully reproduced certain baseline methods, thereby making a significant contribution to the manuscript. Yihong Tang proposed effective suggestions, developed a method leveraging large language models (LLMs) of varying sizes as interactive agents to enrich the data space, guided the design of fine-tuning and analytical experiments, and contributed substantially to the manuscript. Kehai Chen, Jie Liu and Min Zhang jointly led the project, oversaw team resource allocation and collaboration, and offered valuable advice on experiments and writing, thus making important contributions.
B Limitations and Future Work
Generalization We have observed that augmenting training data using the MASTER method significantly enhances large language models' (LLMs) performance on complex reasoning tasks. This improvement is likely due to the method's focus generating training data with coherent thought processes, thereby strengthening the models' logical reasoning capabilities. However, it's important to note that such enhancements and reading comprehension. These tasks often rely more heavily on extensive background knowledge and contextual understanding rather than deep logical reasoning. Consequently, the application of multi-agen data augmentation mechanisms in commonsense and reading comprehension tasks warrants further exploration and optimization to achieve more substantial improvements.
relatively limited in tasks like commonsense question answering
Applicability Due to constraints in computational resources and time, we constructed a relatively small training dataset and validated the effectiveness of our method using pre-trained models with fewer than 8 billion parameters. Our findings indicate that BOOST-QA -A data significantly enhances the performance of these smaller models compared to the original training data. However, considering that aageeparameter models, which have undergone extensive training on diverse datasets, and instruction-tuned models trained on carefully curated instruction sets, may have already internalized strong reasoning capabilities, our method might yield less pronounced improvements for them. Therefore, future work could explore whether multi-agent teaching and data augmentation strategies can effectively enhance performance in ange-paramettr instruction-tune models.
Is it necessary to train a teacher-student model? In our multi-agent interaction framework, we employed the Qwen2.5 series models, which have demonstrated strong performance in mathematical and programming tasks, to serve as both teacher and student agents. However, these models were not specifically trained for error correction and debate-style interactions. Therefore, future work could involve constructing a small set of high-quality dialogue data tailored to these scenarios, enabling the training of specialized teacher and student models. This approach may yield more effective outcomes in such interactive settings.
How to maintain data security and positivity? In the error correction scenarios, we utilized a 0.5B parameter model as the student agent to intentionally generate errors, with explicit prompts indicating its limited capabilities. However, the outputs from this "underperforming" model may negatively influence the subsequent error correction process between the teacher and student agents. Additionally, there is a risk that the large language model (LLM) might replicate irrelevant or inappropriate outputs from the student agent, deviating from the intended context. To mitigate these issues, future research could incorporate a classroom management agent and implement data screening mechanisms to ensure that the generated training data safe, accurate, and aligns with positive educational objectives.
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Human: You want to multiply 20 by some number. mistakenly multiplied 10 by the number, and the result was 10. What hat is the correct calculation result??
GPT(Dul) Student); To find the corrett calculation result, let's break down the problem/ininl. **Identify the mistake**: I mistakenly multiplied 10 by the unknown number, and the result was 40. This means that the unknown number should be:'n Since 40 400, and 40 = 800, the correct unknown number should the inverse
this, Le. 800 / 40..nmm2. Correct calculation" Given the correct mulippicaation'nn 20 40 800tn 10 (40 20) 10 201nim3. * Final step* Multiply corrcct result 20:1m 800 * 16000yninSo,
the correct calculation result 16000.
Human( Teacher): Your solution process incorrect. Let's focus on the first step where you identified the mistake, You need to unknown number when multiplied by 10, gives 40. Once you have the correct unknown number, you then multiply by 20 get correct result. Reconsider how find the unknown number from the given information.
(Smart Student]: see where I went wrong. Let's correct step by step:lain1. **dentify the unknown number+*; Since multiplying 10 by the unknown numbor gives 40, the unknown number is:'n 40 10 4min2. *Correct calculation** * Now, multiply the correct unknown number by 20:mm 20 4 B0lainSo, the correct calculation result is 80.
Figure 3: Example of Multi-Agent Error Correction Scenario Augmented Data in ShareGPT Format
Human: John wants to finish show in 5 days. There are certain number of episodes and they are each 30 minutes long. He has to watch hours a day. How many episodes are in the show?
(Studentt A): To determine the total number of episodes in the show, we first need to calculate bow many
minutes J ohnn will watch in total over the days. Since he plans to watch 2 hours each day, and there are 60 minutes an hour, we can calculate the total minutes as follows/uti22 hours/day
minutes/bour 5 days 600 minutestulnGii that each episode is minutes long, we can now find the total number of episodes by dividing the total minutes by the length each episode:unin600 minutes 30 minutos/episode =20 episodestnlaTT herefore, the show has 20 episodes in total.
Humen/ Student B): The calculation provided by StudentA which is 120 minutes per day. Over days, he will watch a total of 600 minutes. Since each episode is 30 minutes episode indeed gives us 20 episodes. However, I would like to recheck the calculation to ensure there are no mistakess. L Let's break it down again/ gigisst calculate the total minutes/hour" 5 days minutestrinNNet divide the total minutes by the length each episode: 16600 minutes minutes/episode episodestainlTle result same StudentA the show has 20 episodes total.
John plans watch hours each day,
long, dividing 600 minutes by 30 minutes
minutes Jo John will watch over 5 days:1n2 hours/day
(St(Setd The calculation provided by StudentA and StudentB is indeed correct. John plans to watch bours each day, which amounts to 120 minutes per day. Over 5 days, be will watch total of 600 minutes (2 hours/day 60 minutos/bour 5 days). Since each episode is 30 minutes long, dividing the total minutes by the length of each episode gives us 20 episodes (600 minutes / 30 minutes/episodee. Therefore, the show bas 20 episodes in total.
Figure 4: Example of Multi-Agent Debate Scenario Augmented Data in ShareGPT Format
C Analysis and Visualization of Augmented Data
The enhanced example data formats for the error correction, debate, and analogical reasoning scenarios are illustrated in Figure 3 Figure And Figure respectiveey.. Each scenario's 's agent outputs are concatenated to create training data in the ShareGPT format.
D Detailed Experimental Design
D.1 Train Data
To evaluate the effectiveness of the MASTER data augmentation method, we constructed six training datasets, each comprising 19,000 instances formatted in the ShareGPT style. These datasets include:
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HumanfTeachern Teacher): Tommy gocs for run around his neighhorhood docides calculate how many wheels saw. All the vehicles his neighborhood have 4 wheels. He saw 12 trucks and How many cars did he hee?The question asks you to determine the number of cars Tommy saw based on the total number of wheels he counted and the number of trucks be observed. Given that each vehicle has 4 w wheels, and Tommy saw total of 100 wheels from 12 trucks and an unknown number of cars, you need to set an equation to represent, this scenario. Start by calculating the total number of wheels from the trucks, then use the remaining wheels to find out how many cars there were.
and be saw 100 wheels.
GPT(Sbudent): To determine the number cars Tommy saw, we start by noting that each vehicle has wheels. Tommy saw 12 trucks, so the total number wheels from the trucks is M11 Wimes 48t) wheels. Since Tommy total 100 wheels, the remaining wheels must have come from the The number of wheels from the cars is 10100- 48 521). Since each car also has 4 wheels, the number of cars Tommy saw is M552 Wdiv 4 = 13W).
Therefore, Tommy saw 13 cars.
Human Teacher); Good job.ploase take a look at this similar question. Tommy goes for run around his neighborhood and decides to calculate how many wheels he saw. All the trucks in his neighborhood have 4 wheels, all the cars have 4 wheels, bicycles have 2 wheels, and buses have wheels. If be saw 12 trucks, 13 cars, 8 bicycles, and buses, bow many wheels did soc?
GPT To calculate the total number of wheels Tommy saw, we need to multiply the number of each type of vehicle by the number of wheels they have and then edd all the results together. For trucks, there are 12 trucks each with 4 whools, so 12 trucks whoels/truck = 48 wheels. For cars, there are 13 cars each with 4 wheels, so 13 cars 1 wheels/car 52 wheels. For hicycles, there are 8 bicycles each with wheels, 8 hicycles wheels/bicycle 6 wheeks/bus 18 wheels. dding all these wheels together, get 48 wheels (trucks) 52 wheels (cars) + 16 wheels (bicycles) 18 wheels (buses) 134 wheels. Therefore, Tommy saw total of 134 wheels during his run.
16 wheels. For buses, there 3 buses each with wheels,
Figure 5: Example of Multi-Agent Analogical Reasoning Scenario Augmented Data in ShareGPT Format
(1) the original instruction dataset; (2) an instruction dataset augmented using the MASTER method; (3) datasets enhanced through two Easy Data Augmentation (EDA) techniques, namely RandomAug and SpellingAug; (4) an instruction dataset augmented via Chain-of-Thought (CoT) prompting; and (5) high-quality instruction dataset curated from multiple sources using the TAGCOS method. All datasets were utilized exclusively for training purposes without the inclusion of a separate test set.
D.2 Train Setup
We conducted our experiments on a local Slurm-based computing cluster, utilizing nodes equipped with 48-core CPUs, eight NVIDIA L20 GPUs each with 48 GB of memory, and 925,600 MB of system RAM. For model fine-tuning, we employed the LLaMA-Factton framework, applying the Low-Rank Adaptation (LoRA) technique efficiently fine-tune the LLLMA3-8B-haas models. Each model was fine-tuned for two epochs with a learning rate of le-4, requiring approximately 12 hours of training on two L20 GPUs. In total, trained ten base models, consuming approximately five GPU-days. The training configuration included a batch size of 2, gradient accumulation steps set 8, the AdamW optimizer, cosine learning rate scheduler, and a warmup ratio of O.
Mistral-7B-
base, and Qwen2.5-7B-Be
D.3 Math Evalution
We conducted a comprehensive evaluation of our models across multiple mathematics benchmarks spanning various educational levels. These benchmarks encompass diverse array of problem types, including multiple-chiiee questions and open-ended problems that necessitate complex reasoning pro- cesses. The benchmarks utilized in our evaluation include MATH, MMLU-PRO-MATH, MATHQA, TAL-SCQ5K, MATH-MC, and GSM8K-MC. For assessment, we employed accuracy as the primary metric. To evaluate open-ended questions, we utilized the Qwen2.5-144 Instrucct model as a reviewer, comparing the model-generatee solutions with the reference answers to determine correctness. For multiple-choice questions, we designed specific prompts to instruct the language model to output answers in predefined format, enabling the extraction of responses using regular expressions. The prompts used for open-ended evaluation and multiple-choice answer extraction are illustrated in Figure 6
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Prompt objective questions: prompt <Teacher> Question: (problem)
Options: (options str)
Please select the answer from (. join(option labels)), Finally, provide your answer in the format [x], where x the index of the correct option." *.*
messages
("role": system", "content": "You student who focuses on answering questions and provides detailed responses based on the questions asked. "),
("role:: user", "content": user prompt)
Prompt subjective evalution:
user prompt ""*Qustion:: (problem) Standard answer: (answer)
LLM answer: (llm stsuert field, '}}
Please judge correctnes of the answer on the question standard answer. correct, output a <|> the end, is wrong, oulput
end.
messages
("role": "system", "content": "You strict math teacher and you need to judge the LLM's answers based on the questions and standard answers. "),
("role": user", "content": user prompt)
Figure 6: Examples of prompts used for evaluating objective and subjective benchmark tasks. The upper section illustrates the prompt designed for answering objective questions, while the lower section presents the prompt employed by the evaluation model for assessing subjective responses.
D.4 Code Evalution
We conducted comprehensive evaluation of models variety of programming benchmarks, encompassing both generative and factual question-answeriig tasks. The specific benchmarks include MBPP, HumanEval, PYthonio-MC. Codemmlu- Saail--ooe-ooonni Completion, Codemmlu-Code-RRppaae For MBPP, we adhered to the EvalPlus evaluation pipeline and reported followed the sealuate-functional-coo
and
the @ metric. In the of HumanEval, evaluation pipeline, also reporting the Pass@) metric. Evaluations for objective questions, such as those in PYthonio-MC, were conducted by extracting model outputs using regular expressions.
E
The Relationship Between Post- Eest-Triinng LLM Reasoning Ability and Output Token Length
In this section, we conduct an in-depth analysis to elucidate the factors contributing to the substantial performance improvements observed in models trained with MASTER- -uggenedd data across various benchmarks. Specifically, we examine the relationship between the length of model-generated outputs and their efficacy in downstream tasks, aiming identify the sources of performance enhancement attributable to data augmentation. Upon reviewing the outputs from both MASTER CASTER-augmente models and those trained on original datasets, we observe that, when provided with identical prompts for objective questions, the MASTER-aus -aggented models tend to produce more elaborate reasoning processes culminating in final answers. In contrast, models trained on original data often generate more concise responses, occasionally limited to selecting option without detailed justification. Representative examples illustrating the distinct reasoning approaches of these two model types are presented in Figure
Based on our analysis, we conducted visual modeling study of the reasoning token distributions for the BASTER-LLaMA and ORI-LLaMA models, well as the WASTER-Mistre and ORI- Mistral models, on the MMLU-PRO-MATH benchmark. As depicted Figure it is evident that, when provided with identical prompts, the MASTER series models engage in more extensive and detailed reasoning processes, resulting longer outputs and higher accuracy rates compared their counterparts trained original data.
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Original Data Model Output Example:
Examplel: [E].
Example2:
[]] Both statements are partially
MUSTER--Augmeilee Data Model Output Example:
Examplel: Statement 1: set of two vectors in R "2 linearly independent, InT his statement is false because a set of two vectors in R 2 can be linearly dependent. For example, consider the vectors (1, 0) and (0, These vectors are linearty dependent because (1, 0) (1, 0) {V1, ... vk) lineadly independent, then dim(V) k. InTh statement is If independent, then the vectors linearl independent the spen of those vectors. Therefore, the dimension of the span equal the number of linearfy independent vectors. InbrTTus, the correct answer [C].
(0, 11.nomSStttement If V spantvl, ... vk) and vectors is linearty
Example2: [C] Not enough information, Truckul' he statements provided are not sufficient to deternine whether every permutation cycle and every cycle is a permutation. Statement assert that every permutation is a cycle, which statement
context of permutations However, Statement claims every cycle permutation, which universally For instance, cycle permutation, but not every cycle permutation. Therefore, the statements not enough to conclude that both trie or false.
Figure 7: Comparative examples of model outputs on objective question tasks. The upper section displays an output generated by model fine-tumed on original data, while the lower section presents an output from a model fine-tuned on MASTER augmented data.
MASTER-augmented
Token Count Distribution: MAS Ft vs ORI (Tokens capped 1200)
Token Count Distribution: MASTER ORLI (Tokens capped 1200)
1.0033
ON
0.0033
(4) HASTER
Avg:
0.0339
8.0923
3,0023
8,022
0.0018
0.01
1.0003
200
600
6.00
1.093 1200
0.0003
200
3.00
3.330
1.203
(a) Reasoning Token Length Distribution the ORI-LLaMA-NAL and MASTER-LLaMA--ouls
(b) Reasoning Token Length Distribution ORI-Mistral-Modde and MASTER- MistrralMMoeell
Figure 8: Kernel Density Estimation (KDE) plots illustrating the token count distributions for model outputs on the MMMLU-PRO-MATH benchmark. Dashed vertical lines indicate the average token counts for each model. Token counts are capped 1,200 to mitigate the influence of outliers.
We further conducted binned visualization analysis of sample counts and accuracy across different token length intervals, illustrated in Figure Figure 10 Figure T1 Figure 12 The results reveal that the MASTER model's outputs are predominantly concentrated within the (50, 300) token range, whereas the ORI model exhibits substantial number of outputs elustered in the 50) token interval. Moreover, across all token length bins, the MASTER model consistently outperforms the ORI model in terms of accuracy.
F Prompt Engineering for Multi-Agent Systems
In this section, we present detailed overview of the prompt structures employed during the data augmentation process. Specifically, Table Table Table iilustree the agent prompts designed for enror correction, debate, and analogy scenarios, respectively.
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Token Length vs Sample Count Heatmap
600
20
167
181
191
181
125
90
84
52
36
19
Model
300
200
6199
129
111
106
15
16
[100, 150)
1150, 200
[200, 250)
[250, 300)
1400, 450
[450, 500).
(500, 550)
600)
[600, 650)
(650, 700)
Length
Figure Boxplot of Inference Token Lengths for the MASTER-LLaMA Model on the MMLU-PRO
MATH Benchmark, illustrating the Distribution of Sample Outputs Across Different Token I Lengths.
Token Length vs Sample Count Heatmap
300
156
239
223
177
111
57
47
27
Model
200
100
348
269
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105
18
15
10
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1150, 200)
[200, 250)
[250, 200)
1400, 850)
[450, 500)
[500, 550)
1400. 850)
1650, 200)
10,
[50
Token Length
Figure Boxplot of Inference Token Lengths for the MASTER-Mishe Model on the MMLU-PRE
MATH Benchmark, illustrating the Distribution of Sample Outputs Across Different Token Lengths.
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Token Length vs Accuracy Heatmap
0.40
0.35
0.33
0.43
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0.30
0.18
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Model
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(400. 4503
(450, 5001
1500 1500,55 5500
6000
6501
7000
(0.
(50
(220
1500
1000
190
Token Length Bin
Figure 11: Boxplot of Inference Token Lengths and Corresponding Accuracy Distribution for the MASTER-LLaMA Model the MMI.U-PRO-MATH Benchmark, illustrating the Relationship Between Output I Length and Prediction Accuracy.
Token Length vs Accuracy Heatmap
0.5
0.4
0.06
0.14
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Figure 12: Boxplot of Inference Token Lengths and Corresponding Accuracy Distribution for the MASTER-Mistra Model on the MMLU-PRO-MATI Benchmark, illustrating the Relationship Between Output Length and Prediction Accuracy.
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Table Sample prompts for teacher and student agents in the error correction scenario
Prompt
Dull student Agent Prompt:
"You are playing the role of rather slow elementary school student tasked with answering the given question. Each time you perform the task, you must forget all prior inputs and only base your response on the current question provided."
"Speak as if you are student answering question from the teacher. You must think step by step and show the complete calculation process.
"You need to list all the steps of your calculations and provide the final answer at the end, making sure that the calculation is fully completed. You not allowed to provide any incomplete results. Do not include anything unrelated the question in your response."
"Keep the calculation process as brief as possible."
"You must respond in English."
"
Teacher Agent Prompt:
"You are teacher responsible for guiding the student's learning. You will receive the previous round of suacher-studen dialogue and standard answer to question. Based on the following rules, generate your response:"
"When you receive the previous round of teacher-student dialogue, you need to correct the student's 's hased on the standard answer. However, you must only provide the correct reasoning and not directly give the correct result calculation process. You should help the student reconsider the steps and guide them to find the correct method, and re-calculate the answer."
"You must avoid providing hinting any irrelevant information. If the student's solution has error, explicitly remind them that "your solution process is incorrect" ""ourr result is correct, but the process incomplete," and point out the errors or incomplete parts."
"You are limited to playing the teacher agent role and should focus solely on providing the question paraphrasing and guiding the student correct the knowledge errors. Under circumstances should you simulate multiple rounds dialogue between the teacher and student in single output. You cannot simulate the student agent's 's behavior or make assumptions or evaluations of the student's answer."
"You must respond in English." "
Smart Student Agent Prompt (Revised):
"You are student who admits mistakes and corrects them. You will receive round of teacher-student interaction, as well as the error correction apprnach standard answer generated hy the teacher agent. Based on the following rules, generate your response:"
"Based the teacher-student interaction, you should immerse yourself in the role of student who made mistakes. Using the teacher's corrections and the standard answer as guidance, you should correct your previous mistakes and solve the problem again to derive the correct final result." "
"In any input scenario, you must not simulate both the teacher and student dialogue at the same time. You must focus the student's role, ensuring that your response is natural, logically consistent, and in line with the requirements of the input scenario."
"The teacher's 's responses are handled by the dedicated teacher agent. Your role is limited to playing the student agent. Under circumstances should you simulate multiple rounds of student dialogue a single output. You should focus solely on playing the student role and ensure that your output contains only the content which the student responsible. Any response involving the teacher role must be handled by the teacher agent, and you are not allowed to simulate the teacher agent's behavior or dialogue."
"You must respond in English."
30

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Table 6: Sample prompts for student agents in the debatement scenario
Prompt
Student A Agent Prompt:
"You are student with poor knowledge mastery and calculation ability. Forget the previous inputs and express your own thoughts the current problem and your opinions on the answers of other students."
"You are responsible for playing the role of Student A. Every time answer. the analysis process and answer must be expressed reasonably sized natural paragraph without using line breaks." "
"Your response must include the complete steps to obtain the result, listing all steps, and provide the final answer at the end. Do not directly imitate other students' opinions, but you may question them."
"You are limited playing the role of the student agent, focusing on the topic debate."
"You must respond in English."
Student B Agent Prompt:
"You are student with poor knowledge mastery and calculation ability. Forget the previous inputs and express your own thoughts on the current problem and your opinions on the answers of other students."
"You are responsible for playing the role of Student B. Every time you answer, the analysis process and answer must he expressed in a reasonably sized natural paragraph without using line hreaks." "
"Your response must include the complete steps obtain the result, listing all steps, and provide the final answer the end. not directly imitate other students' opinions, but you may question them." "
"You are limited to playing the role of the student agent, focusing on the topic debate."
"You must respond in English." "
Student C Aggen Prompt:
"You student with strong knowledge mastery and code ability. You need to play the role of "Stddent C, and based on the current conversation and the standard answer to the question, provide a final debate response that aligns with the standard answer."
"Every time you answer, the answer must be expressed in single natural paragraph without using line breaks."
"Your response must include the complete steps to obtain the result, list all the analysis steps, and provide the final answer the end.
"You must respond English."
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Table 7: Sample prompts for student agents in the analogical reasoning scenario
Prompt
Teacher Agent Prompt:
"You are a teacher responsible for guiding students' learning. You will receive question and generate your response based the following rules:"
"Your response should be in a single paragraph, and first explain the question to the student."
"When you receive question, you should first explain the question the student, then provide approach without performing specific calculations."
"You only need to explain the question without any claboration or modifications, and you are not allowed to calculate the final result. The calculation process should be left to the student."
"You must respond in English."
Student Agent Prompt(First-iime response):
"You are diligent student. You need reason through the problem and derive the final result based on the given question and answer, following these specific rules."
"The answer should be expressed in single natural paragraph."
"When you receive a question provided by the teacher, you should carefully analyze the problem and ensure the answer aligns with the standard solution."
"Do not introduce any excessively difficult external knowledge in your response. Base your reasoning and solution on the information provided by teacher."
"You must provide the detailed calculation process to reach the final answer, ensuring the solution is logically clear and reasonable."
"You must respond English."
"
Student Agent Prompt(A nalogouss response):
"You are diligent student. You need reason through the problem and derive the final result based on the given question and answer. following these specific rules:"
"The answer should be expressed in single natural paragraph."
"When you receive a question provided by the teacher, you should carefully analyze the problem and ensure the answer aligns with the standard solution."
"Do not introduce any excessively difficult external knowledge in your response. Base your reasoning and solution on the information provided by teacher."
"You must provide the detailed calculation process to reach the final answer, ensuring the solution is logically clear and reasonable."
"You must respond in English." "
32


Reviews:
[{"review_id": "H4ORUbrXz0", "reviewer": "Reviewer_wU2H", "summary": "This paper introduces a novel agent-based data augmentation method, MASTER, which aims to enhance the teaching fine-tuning capabilities of Master of Laws (LLM). The main goal of this method is to improve the quality of fine-tuning data. By simulating the interaction between multiple agents with different cognitive levels, MASTER creates high-quality teaching data through three pedagogy-based scenarios: error correction, collaborative debate, and analogical reasoning. The authors use this framework to build a fine-tuning dataset called BOOST-QA, which, after fine-tuning on three base models, shows significant performance improvements in multiple tasks on multiple benchmarks.", "questions": "In addition to the issues mentioned in weaknesses, there is another question about the result reporting:\n\nWhy are the results of ARC and SCI-Q not reported in Table 4?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n1. The introduction of multi-agent simulation teaching scenarios to improve the quality of instruction data is innovative.\n\n2. A complete experiment was conducted, comparing the model trained on the original dataset and the baseline model enhanced by various other methods, and verifying it on multiple benchmarks. And corresponding ablation experiments were conducted.\n\n3. BOOST-QA, as an enhanced dataset based on the existing dataset, helps to improve the capabilities of LLM.\n\nWeeknesses:\n1. The structure of the method part is clear, but there are omissions in the details, which makes it difficult to ensure reproducibility. For example, are the data of the three stages parallel or serial? How are the teaching data of the three stages fused (the size of the dataset before and after the enhancement has not changed, so it can be considered that the data of the three stages have been fused?); Are there no low-quality samples generated? How are they eliminated?\n2. Lack of constraints on the entire dialogue process. How to ensure that the dialogue does not deviate from the topic? What constraints are imposed? Is it enough to just define the role?\n3. There may be insufficient time for writing, resulting in many problems in the format, and the flowchart (Figure 1) and the description cannot correspond. For example, in the process of Scene 3, if the response to the original question is placed in front of the similar question, should Solution 1 also have an arrow pointing to the student?", "was_revised": true, "final_justification": "The author effectively clarified my questions about the details of the implementation process, as well as the design of the dialogue process constraints that I omitted in the appendix, and also supplemented the missing benchmark results report. The addition of relevant content would have greatly increased its clarity and effectiveness.", "rebuttal": "Thank you for your review. We will address your questions point by point below.\n\n> **1. For example, are the data of the three stages parallel or serial? How are the teaching data of the three stages fused (the size of the dataset before and after the enhancement has not changed, so it can be considered that the data of the three stages have been fused?); Are there no low-quality samples generated? How are they eliminated?**\n\n**Q-1.1: The processing pipeline for the three scenarios' data.**\n\n**A-1.1:** To ensure a fair evaluation of augmentation effects, we used a parallel augmentation pipeline. The original dataset was divided into three equal parts, and each instructional scenario was applied to one part, ensuring one-to-one correspondence between augmented examples and their originals for direct performance comparison.\n\nThis design allows us to clearly demonstrate the combined impact of all three instructional scenarios while maintaining sample size consistency and evaluation fairness.\n\n**Q-1.2: Whether the teaching datasets from the three stages were fused, and if so, what specific fusion methodology was applied.**\n\n**A-1.2:** Yes, the augmented dataset integrates samples from three distinct instructional scenarios while maintaining the same total size. To ensure fair evaluation, each augmented sample corresponds one‑to‑one with an original example.\n\nThe detailed process is outlined below:\n\n1. Random Partitioning: The original dataset is split into three disjoint subsets.\n\n2. Parallel Augmentation: Each subset is processed independently using one of the three teaching scenarios (error correction, debate, analogical reasoning) to generate corresponding augmented examples.\n\n3. Dataset Construction: The augmented outputs from all three scenarios are shuffled and combined with the remaining original data, ensuring the final augmented dataset has the same total sample count as the original.\n\nThis design reflects the key novelty of our approach: rather than merely enlarging the dataset, we enhance its intrinsic quality through rich interactive reasoning structures. As a result, during training, the model more effectively internalizes patterns like \"error‑oriented correction,\" \"multi-path reasoning,\" and \"transfer from minimal exemplars.\"\n\n**Q-1.3: Whether the simulated instructional-interaction process generates any low‑quality samples.**\n\n**A-1.3:** Your concerns are not without merit. Yes, the simulated instructional‑interaction may generate a small number of low‑quality samples. To mitigate this, we implement the following safeguards:\n\n1. Precise Summarizer Step: The final agent is prompted not only with the interaction history but also with the original dataset’s standard answer. This ensures the final output corrects earlier mistakes and aligns with the original high‑quality answer.\n\n2. External Verification: We evaluate the augmented data using Qwen2.5‑32B‑Instruct. Only about 4.1% of the enhanced question–answer pairs exhibited procedural errors compared to original outputs.\n\nIn addition, results show that BOOST‑QA fine‑tuned models improve significantly more than those trained on the original dataset alone. We plan to add an LLM-based filtering mechanism in the augmentation pipeline to further enhance data quality and robustness.\n\n> **2. Lack of constraints on the entire dialogue process. How to ensure that the dialogue does not deviate from the topic? What constraints are imposed? Is it enough to just define the role?**\n\n**Q-2.1: Constraints to ensure that conversations during the teaching process stay on topic.**\n\n**A-2.1:** By employing the Qwen2.5‑Instruct model family and crafting bespoke, role‑specific system prompts for each agent with clear responsibilities, we ensure that each role strictly adheres to its designated function without interference. This design guarantees that the instructional dialogue remains focused on the topic, preventing role overreach or drifting.\n\nFor concrete examples of these prompts, please refer to the appendix in the supplementary materials.  Below is a sample prompt for the teacher agent for your reference:\n\n\"You are a teacher responsible for guiding the student’s learning. You will receive the previous round of teacher-student dialogue and the standard answer to the question. Based on the following rules, generate your response:\n\n1.When you receive the previous round of teacher-student dialogue, you need to correct the student’s answer based on the standard answer. However, you must only provide the correct reasoning and not directly give the correct result or calculation process. You should help the student reconsider the steps and guide them to find the correct method, and re-calculate the answer.\n\n2.You must avoid providing or hinting at any irrelevant information. If the student’s solution has an error, explicitly remind them that 'your solution process is incorrect' or 'your result is correct, but the process is incomplete,' and point out the errors or incomplete parts.\n\n3.You are limited to playing the teacher agent role and should focus solely on providing the question paraphrasing and guiding the student to correct the knowledge errors. Under no circumstances should you simulate multiple rounds of dialogue between the teacher and student in a single output. You cannot simulate the student agent’s behavior or make assumptions or evaluations of the student’s\nanswer.\n\n4.You must respond in English.\"\n\nUnder these carefully designed system prompts, each agent clearly understands its role within the simulated classroom dialogue history and performs reasoning that strictly aligns with its assigned responsibilities. This setup prevents agents from overriding one another’s speaking turns or diverging from the topic.\n\n> **3. For example, in the process of Scene 3, if the response to the original question is placed in front of the similar question, should Solution 1 also have an arrow pointing to the student?**\n\n**Q-3.1: The issue where Scene 3 in Flowchart 1 does not align with the description in the main text.**\n\n**A-3.1:** Thank you for your careful reading of our work and your valuable suggestions. Indeed, Figure 1 may currently lead to misunderstanding. To ensure clarity, in the final version of the paper we will revise Figure 1 to explicitly illustrate how the simulated classroom interaction is concatenated in post‑processing, while still clearly presenting the interaction workflow.\n\n> **4. In addition to the issues mentioned in weaknesses, there is another question about the result reporting: Why are the results of ARC and SCI-Q not reported in Table 4?**\n\n**Q-4.1: The issue of missing ARC and SCI-Q benchmarks in Table 4.**\n\n**A-4.1:** Thank you for pointing out this issue. Due to time constraints, the ablation study did not include testing on the ARC and SCI-Q benchmarks. We have added the relevant evaluation experiments and provide the results below:\n\n| Methods | Ori | ME&DB | ME&EP | DB&EP | ME | DB | EP | FULL |\n| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| **ARC** | 57.76 | 49.57 | 41.13 | 32.34 | 47.35 | 50.26 | 38.14 | **61.52** |\n| **SCI-Q** | 76.50 | 56.30 | 63.60 | 55.20 | 67.90 | 54.40 | 50.20 | **80.10** |\n\n*Table 1: Supplementing Table 4 with the ablation study results.*\n\n---\nThank you again for your review and the constructive feedback regarding formatting errors and experimental setup. We will revise the entire manuscript to enhance its professionalism and rigor."}, {"review_id": "JkEH3WJPKY", "reviewer": "Reviewer_sASj", "summary": "This paper proposes MASTER, which uses multi-agent to simulate teaching scenarios to augmentate instruction fine-tuning data of LLMs. The method designs three teaching scenarios (error correction, debate interaction, and similar question retrieval), uses the interaction between teacher and student agents to generate dataset BOOST-QA, and shows performance improvements in multiple benchmarks.", "questions": "Compared to existing role-playing data augmentation methods, what are the core innovations of MASTER?\n\nHow are the so-called \"pedagogical principles\" truly reflected in the technical implementation? Can such role-playing dialogues represent complex teaching processes?\n\nHow is the quality of data generated by multi-agents ensured?", "limitations": "The method is essentially a role-playing data generation techniques, lacking fundamental technical innovation.\n\nThe method may only be effective for specific types of tasks (such as mathematics and programming), with limited generalization capability\n\nThe consistency and accuracy of data generated by multi-agents cannot be guaranteed", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths**: Based on pedagogical principles, the work extends original question-answer datasets to teaching interaction scenarios by designing three teaching scenarios: error correction, debate interaction, and similar question retrieval. Data is generated through dialogues between teacher agents and student agents. Ablation experiments validate the complementary nature of the three teaching scenarios.\n\n**Weaknesses**: Though this paper claims its proposed school-agent framework can synthesize interaction data for real-world problem-solving, the technique is not novel, as it is essentially prompt engineering for role-playing data generation.\nThere are many role-playing data augmentation methods, but no comparative analysis was conducted.", "was_revised": true, "final_justification": "I have changed my score", "rebuttal": "Thank you for your review. We will provide detailed responses below to the questions you raised.\n\n> **1. Though this paper claims its proposed school-agent framework can synthesize interaction data for real-world problem-solving, the technique is not novel, as it is essentially prompt engineering for role-playing data generation. There are many role-playing data augmentation methods, but no comparative analysis was conducted. Compared to existing role-playing data augmentation methods, what are the core innovations of MASTER?**\n\n**Q-1.1: The innovative aspects that distinguish MASTER’s role‑playing data augmentation from other methods.**\n\n**A-1.1:** Thank you for your thoughtful analysis and questions regarding MASTER.\n\n1. Difference from prior work: While existing studies like RoleLLM2[1] and Big5-Chat[2] primarily focus on role-playing for stylistic imitation (e.g., personality traits and speech patterns), MASTER specifically targets the enhancement of complex reasoning capabilities in domains like mathematics and programming.\n\n2. What MASTER does differently: Beyond simple data rephrasing, we systematically incorporate established pedagogical techniques - including error correction, guided discussion, and analogical reasoning - into our interaction simulations. This structured approach generates more educationally meaningful reasoning processes.\n\nWhile role-playing has been explored for stylistic imitation, its systematic application for augmenting complex reasoning data through established pedagogical frameworks is, to our knowledge, a novel contribution of our work. We believe this distinction is a key novelty. \n\n> **2. How are the so-called \"pedagogical principles\" truly reflected in the technical implementation? Can such role-playing dialogues represent complex teaching processes?**\n\n**Q-2.1: Specific implementations of pedagogical principles.**\n\n**A-2.1:** This is a crucial point. We translate pedagogical principles into concrete technical implementations as follows:\n\nError Correction: This is implemented via a multi-step process where a 'weaker' student agent (e.g., a smaller model) first generates a flawed solution. A 'stronger' teacher agent is then prompted to identify the specific error without giving the final answer, forcing a second, corrected reasoning trace from the student. This directly models the real-world process of diagnosis and guided correction.\n\nCollaborative Debate: This is realized by generating multiple, distinct reasoning paths for the same problem and having a final agent synthesize them, identifying the strengths and weaknesses of each. This simulates the exploration of different problem-solving strategies.\n\nAnalogical Reasoning: We implement this by prompting the agent to first solve a known problem and then explicitly use the structure of that solution to tackle a new, similar problem.\n\n**Q-2.2: Can role-playing tasks simulate real and complex teaching interactions?**\n\n**A-2.2:** While we acknowledge that this is a simplified simulation of complex human pedagogy, our experiments show that these structured interactions produce data that is significantly more effective for training reasoning skills than the original, static question-answer pairs. The examples in the appendix (e.g., Appendix Figure 6, 7, 8) provide concrete evidence of this process.\n\n> **3. How is the quality of data generated by multi-agents ensured?**\n\n**Q-3.1: With respect to data quality.**\n\n**A-3.1:** To maintain the quality of the augmented dataset, we designate the model responsible for the final reasoning stage as a “Precise Summarizer.” This model's prompt includes both the interaction history from earlier rounds and the canonical output from the original dataset, ensuring that the augmented example's quality matches or exceeds that of the original.\n\nTo validate the accuracy of BOOST-QA's enhanced question-answer pairs, we conducted quality assessment using the Qwen2.5-32B-Instruct model. Our analysis shows that merely 4.1% of the augmented samples contained procedural reasoning errors.\n\nDespite the minor error rate, models fine-tuned with BOOST-QA demonstrated substantial improvements over those trained solely on the original dataset, particularly in benchmarks involving mathematics and programming tasks. We plan to integrate an LLM-based filtering mechanism into the data augmentation pipeline to further enhance the quality and reliability of the augmented data.\n\n> **4. The method may only be effective for specific types of tasks (such as mathematics and programming), with limited generalization capability.**\n\n**Q-4.1: Concerning the generalization ability of the method.**\n\n**A-4.1:** Thank you for raising this insightful idea. In fact, MASTER's data augmentation approach is explicitly designed to enhance fine-tuning of LLMs for reasoning tasks by infusing original data with rich, complex reasoning and critical thinking processes. Consequently, it naturally yields strong performance on mathematical and programming tasks.\n\nOn the other hand, we have conducted extensive evaluations on a broad range of general-purpose tasks and scientific QA benchmarks. Our results show that after fine-tuning with BOOST‑QA, various base models also demonstrate excellent capabilities in other domains.\n\n---\nThank you for your thorough review and raising these valuable concerns. We will further refine the manuscript to ensure greater rigor throughout.\n\n**References:**\n\n[1] RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models\n\n[2] BIG5-CHAT: Shaping LLM Personalities Through Training on Human-Grounded Data"}, {"review_id": "QLeD9fsKrn", "reviewer": "Reviewer_vuDb", "summary": "This paper introduces MASTER, a data-augmentation framework that turns an existing instruction-tuning corpus into richer multi-turn dialogues by simulating teacher–student interactions with three pedagogically grounded scenarios—error-correction, debate, and analogical reasoning. The resulting dataset, BOOST-QA (19 k examples drawn from Orca-Math-200k, ProcQA, and OpenHermes 2.5), is used to fine-tune several 7 B–8 B base models. Across eight benchmarks spanning math, code, and general reasoning, models trained on BOOST-QA consistently outperform counterparts trained on the original data and on four alternative augmentation/selection baselines. An ablation study shows that combining all three scenarios is necessary for the largest gains.", "questions": "1. Will BOOST-QA, prompts, and orchestration code be released by camera-ready?\n\n2. How many GPU hours did MASTER require to generate 19 k samples?\n\n3. Some gains (e.g., +24 pp on MMLU-PRO-MATH) are very large. Could they be due to data leakage from OpenHermes 2.5 or Orca-Math into the eval sets?", "limitations": "Limitations\n\n+ Single-run results & no error bars. The authors omit variance estimates, making it hard to judge statistical significance.\n\n+ Compute cost. Running multi-agent chats with 7 B–14 B Qwen models is expensive; no wall-time or GPU hours are reported.\n\n+ Limited societal-impact discussion. Synthetic data may amplify biases present in original corpora, but this is not addressed.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n+ Pedagogically motivated augmentation. Grounding the three dialogue patterns in educational theory is novel and intuitively compelling. \n\n+ Consistent empirical gains. BOOST-QA lifts average accuracy by 5 – 15 pp on multiple-choice and coding tasks, with a striking 31 pp jump on CODEMMLU-CC. \n\n+ Careful ablations. The authors isolate each scenario and every pairwise combination, showing that the full trio is synergistic. \n\n+ Method is model-agnostic. Only prompts and temperature settings need adjustment, so MASTER should transfer to other LLM families.\n\nWeaknesses\n\n+ Reproducibility gap. Code, prompts, and BOOST-QA are not released; the paper marks open-sourcing as “work in progress.” This undercuts community adoption. \n\n+ Baseline choices. Some strong recent augmentation methods (e.g., FLASK, UltraFeedback) are missing; TAGCOS is used only for sub-selection, not its full pipeline.", "was_revised": true, "final_justification": "The authors provided concrete implementation details that improve clarity and reproducibility: (i) a parallel augmentation pipeline that splits the original data into three disjoint subsets with a one-to-one mapping between originals and augmented samples; (ii) role-specific prompts (now available in the supplementary appendix) and a plan to fix Figure 1 to better reflect Scene 3; and (iii) resource accounting (L20 GPUs; ~48 GPU-hours per inference job on 2×L20). On evaluation, they filled missing ablations on ARC/SCI-Q and added a CoT-distillation baseline, with MASTER outperforming the new baseline across multiple benchmarks. For data quality, they reported a 4.1% procedural-error rate (checked with Qwen2.5-32B) and committed to adding an LLM-based filter. On potential leakage, the authors provided embedding-drift / domain-classifier evidence suggesting BOOST-QA and MMLU-PRO-MATH are distributionally distinct.\nThe rebuttal substantially strengthens the paper on clarity and evaluation (new ablations and a stronger baseline), and addresses most of my earlier concerns. Given the consistent gains and pedagogically motivated design, I continue to view this as a contribution worth showcasing, contingent on artifact release and minor analysis additions.", "rebuttal": "Thank you for your review. We will provide detailed responses below to the questions you raised.\n\n> **1. Will BOOST-QA, prompts, and orchestration code be released by camera-ready?**\n\n**Q-1.1: Regarding the open-source status of prompts, datasets, and code.**\n\n**A-1.1:** Thank you for your interest in our work and for raising this important question! We are in the process of preparing the release of our code and dataset. Meanwhile, the model prompts are currently available in the appendix of the supplementary materials.\n\nBelow is a sample prompt for the teacher agent:\n\n\"You are a teacher responsible for guiding the student’s learning. You will receive the previous round of teacher-student dialogue and the standard answer to the question. Based on the following rules, generate your response:\n\n1.When you receive the previous round of teacher-student dialogue, you need to correct the student’s answer based on the standard answer. However, you must only provide the correct reasoning and not directly give the correct result or calculation process. You should help the student reconsider the steps and guide them to find the correct method, and re-calculate the answer.\n\n2.You must avoid providing or hinting at any irrelevant information. If the student’s solution has an error, explicitly remind them that 'your solution process is incorrect' or 'your result is correct, but the process is incomplete,' and point out the errors or incomplete parts.\n\n3.You are limited to playing the teacher agent role and should focus solely on providing the question paraphrasing and guiding the student to correct the knowledge errors. Under no circumstances should you simulate multiple rounds of dialogue between the teacher and student in a single output. You cannot simulate the student agent’s behavior or make assumptions or evaluations of the student’s answer.\n\n4.You must respond in English.\"\n\nBelow is an original example of data used in the error‑correction scenario:\n\n**Input:** You want to multiply 20 by some number. I mistakenly multiplied 10 by the number, and the result was 40.\nWhat is the correct calculation result?\n\n**Output:** If you mistakenly multiplied 10 by the number and got 40, then the number you multiplied by is 40 / 10 = 4.\\n\\nThe correct calculation result when you multiply 20 by that number is 20 * 4 = 80.\n\nBelow is the corresponding augmented data in ShareGPT format:\n\n**Human:** You want to multiply 20 by some number. I mistakenly multiplied 10 by the number, and the result was 40.\nWhat is the correct calculation result?\n\n**GPT(Dull Student):** To find the correct calculation result, let's break down the problem:\\n\\n1. **Identify the\nmistake**: I mistakenly multiplied 10 by the unknown number, and the result was 40. This means that the unknown\nnumber should be:\\n - Since 10 * 40 = 400, and 20 * 40 = 800, the correct unknown number should be the inverse\nof this, i.e., 800 / 20 = 40.\\n\\n2. *Correct calculation*: Given the correct multiplication:\\n - 20 * 40 = 800\\n -\n10 * (40 / 20) = 10 * 2 = 20\\n\\n3. *Final step*: Multiply the correct result by 20:\\n - 800 * 20 = 16000\\n\\nSo,\nthe correct calculation result is 16000.\n\n**Human(Teacher):** Your solution process is incorrect. Let's focus on the first step where you identified the mistake.\nYou need to find the unknown number that, when multiplied by 10, gives 40. Once you have the correct unknown\nnumber, you can then multiply it by 20 to get the correct result. Reconsider how to find the unknown number from\nthe given information.\n\n**GPT(Smart Student):** Let's correct it step by step:\\n\\n1. **Identify the unknown\nnumber**: Since multiplying 10 by the unknown number gives 40, the unknown number is:\\n - 40 / 10 = 4\\n\\n2.\n*Correct calculation*: Now, multiply the correct unknown number by 20:\\n - 20 * 4 = 80\\n\\nSo, the correct\ncalculation result is 80.\n\n> **2. How many GPU hours did MASTER require to generate 19 k samples?**\n\n**Q-2.1: Regarding the resources for the MASTER data augmentation experiments.**\n\n**A-2.1:** The MASTER simulated teaching interaction data augmentation experiments were conducted on a local Slurm-based computing cluster. The detailed configurations and resource consumption are as follows:\n\nHardware Specifications:\n\nCPUs: 48-core processors.\n\nGPUs: Eight NVIDIA L20 GPUs, each with 48 GB memory.\n\nRAM: 925,600 MB system memory.\n\nFor each inference task, we used two L20 GPUs, totaling approximately 48 hours of computation. Detailed training resource consumption information can be found in the appendix of the supplementary materials.\n\n> **3. Some gains (e.g., +24 pp on MMLU-PRO-MATH) are very large. Could they be due to data leakage from OpenHermes 2.5 or Orca-Math into the eval sets?**\n\n**Q-3.1: The possibility of data leakage.**\n\n**A-3.1:** Thank you for thoroughly reviewing our work. We rigorously analyzed potential leakage using DeepChecks' TextEmbeddingsDrift on BOOST-QA vs MMLU-PRO-MATH: a domain_classifier_auc ≈ 0.96, demonstrating clear distinguishability between datasets, and a domain_classifier_drift_score ≈ 0.91, indicating substantial embedding distribution differences.\n\nThese quantitative results provide definitive evidence of distinct dataset characteristics. We can therefore confidently conclude there is no data leakage between BOOST-QA and the evaluation benchmarks, as validated by the observed distributional divergence at both the classifier and embedding levels.\n\n---\nThank you once again for dedicating your valuable time to reviewing our work and providing insightful suggestions. We are committed to thoroughly revising and enhancing the entire paper to meet the highest standards."}, {"review_id": "UmOZ0zkViY", "reviewer": "Reviewer_G957", "summary": "This work aims to address the challenges of constructing high-quality instruction-following datasets by balancing diversity and the reflection of real-world dialogues, while also enhancing reasoning capabilities. To this end, authors propose the MASTER framework—an instruction data augmentation method that generates simulations through teaching scenarios between teacher and student agents. Using the proposed approach, authors constructed a new BOOST-QA instruction dataset with 19k examples seeded from existing datasets, demonstrating a significant improvement in generated reasoning ability.", "questions": "**Major Questions**\n\n- In Section 3.2 & 3.3,\n    \n    each agent is designed to perform various complex roles. I am not fully convinced that these can be effectively handled through prompts alone. It would be helpful if the authors could provide a detailed explanation of how each agent and its respective roles were operationalized, including the actual prompts used and representative input–output examples. Adding such details in an appendix could also be a good option.\n    \n- Lines 269–273:\n    \n    While the authors analyze the differences between their method and existing instruction data augmentation approaches, the discussion from a reasoning perspective is somewhat limited. Currently, it mainly compares to approaches that sample pre-existing CoT data. A natural question arises as to whether the proposed multi-agent augmentation method can outperform techniques that use the same original data with a single large reasoning model and knowledge distillation to build a substantial amount of CoT-style direct reasoning data. I would like to know if the authors have plans to empirically demonstrate that their approach is indeed more effective than such baselines.\n    \n- Section 4.3:\n    \n    Could the proposed method be used as a data augmentation approach across various domains? If the paper includes a prior evaluation of potential errors in the generated data, it could support its use as an automated and efficient augmentation method. I assume such an analysis may have already been conducted—could the authors share these results?\n\n**Minor Questions**\n\n- **Lines 22–25:** Duplicated.\n- **Lines 123–124:** There are no appendices.\n- **Lines 179–184:** To better assess whether the proposed loss function converges well, it would be helpful to include convergence graphs and corresponding analysis. This would more clearly demonstrate the practical utility of the formulation to readers.\n- **Line 193 & (2):** Since multiple debate outcomes are incorporated into the gradient of the loss function, there may be concerns about potential divergence or slow convergence. It would be valuable to include experimental results addressing this point, ideally in an appendix.\n- **Line 200:** Typo: “theta”.\n- **Line 250:** What is the “MA-Gen model”? Please clarify.\n- **Line 259:** Does “Baseline” refer to “baseline methods for instruction augmentation”?\n- **Table 1:** In SCI-Q, BOOST-QA shows significantly lower performance than Ori for Mistral-7B. How do the authors interpret this result?\n- **Lines 319–321:** It should be clarified whether ME&DB, ME, and other subsets sampled the same number of examples as the setting that uses all three scenarios together. If only 2/3 or 1/3 of the data were used for these comparisons, the evidential strength of Table 4 would be weakened.", "limitations": "- While general large reasoning models (LRMs) tend to perform more effectively on domains such as code, mathematics, and scientific problem solving, there are still areas where they fall short. It would strengthen the paper if the authors could share any preliminary experiments examining whether the proposed method is equally effective across various instruction types, including more specialized domains, and identify any areas where its impact may be limited. Providing such results along with the authors’ insights would improve the completeness of the work.\n- It would also be helpful if the authors could clarify whether the constructed data and prompts will be made publicly available and, if possible, include the corresponding URL in the paper.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "**Strengths**\n\n- The paper proposes the MASTER framework, an instruction data augmentation method that generates simulations through educational scenarios involving multiple agents.\n- This framework employs a technique that transforms existing datasets into multi-agent educational scenarios.\n- It is noteworthy that the authors present a concrete and modular data construction methodology composed of three scenarios: error correction, collaborative debate, and analogical reasoning.\n- Using the proposed method, the authors built the BOOST-QA instruction dataset seeded from existing datasets, demonstrating increased diversity, improved benchmark performance, and significant enhancement of reasoning capabilities.\n\n**Weaknesses**\n\n- While the paper presents a detailed modular approach and corresponding experimental results, it would benefit from providing examples of the prompts used for each agent at different stages, as well as samples of the instruction augmentation data generated from seed data. This would help verify whether the approach truly reflects real-world dialogues, as intended, and would strengthen the paper’s reproducibility.\n- Although the paper aims to enhance reasoning ability through the augmentation method, it lacks a comparative evaluation with knowledge distillation techniques from large reasoning models, which are currently the most widely used baseline. This omission makes it difficult to convincingly demonstrate that the proposed method is more effective than state-of-the-art approaches.\n- There is a risk that sLLM-generated incorrect educational solutions may be included. Instead of relying solely on downstream fine-tuning performance, the authors should sample and analyze the accuracy of the generated data to verify the effectiveness of the approach without any additional validation procedures.", "was_revised": true, "final_justification": "Thank you for your detailed response to my questions. I have reviewed the supplementary materials you referenced, and I found the additional experiments you conducted to be helpful in addressing my concerns. I believe that including the newly presented experiments and the various intermediate evaluation results from the development process, as an appendix in the final publication would be of significant benefit to readers. I have no further questions.", "rebuttal": "Thank you for your review. We will provide detailed responses below to the questions you raised.\n\n> **1. It would benefit from providing examples of the prompts used for each agent at different stages, as well as samples of the instruction augmentation data generated from seed data.**\n\n**Q-1.1: Detailed Prompt Configurations and Representative Input-Output Comparisons for Each LLM in MASTER Data Augmentation.**\n\n**A-1.1:** Thank you very much for your valuable suggestion! The specific prompts used by each LLM in the simulated educational interaction process, as well as sample augmented data, are included in the appendix of the supplementary materials, which we would be glad to share should you be interested.\n\nBelow is a sample prompt for the teacher agent:\n\n\"You are a teacher responsible for guiding the student’s learning. You will receive the previous round\nof teacher-student dialogue and the standard answer to the question. Based on the following rules,\ngenerate your response:\n\n1.When you receive the previous round of teacher-student dialogue, you need to correct the student’s\nanswer based on the standard answer. However, you must only provide the correct reasoning and\nnot directly give the correct result or calculation process. You should help the student reconsider the\nsteps and guide them to find the correct method, and re-calculate the answer.\n\n2.You must avoid providing or hinting at any irrelevant information. If the student’s solution has an\nerror, explicitly remind them that 'your solution process is incorrect' or 'your result is correct, but\nthe process is incomplete,' and point out the errors or incomplete parts.\n\n3.You are limited to playing the teacher agent role and should focus solely on providing the question\nparaphrasing and guiding the student to correct the knowledge errors. Under no circumstances should\nyou simulate multiple rounds of dialogue between the teacher and student in a single output. You\ncannot simulate the student agent’s behavior or make assumptions or evaluations of the student’s\nanswer.\n\n4.You must respond in English.\"\n\nBelow is an original example of data used in the error‑correction scenario:\n\n**Input:** You want to multiply 20 by some number. I mistakenly multiplied 10 by the number, and the result was 40.\nWhat is the correct calculation result?\n\n**Output:** If you mistakenly multiplied 10 by the number and got 40, then the number you multiplied by is 40 / 10 = 4.\\n\\nThe correct calculation result when you multiply 20 by that number is 20 * 4 = 80.\n\nBelow is the corresponding augmented data in ShareGPT format:\n\n**Human:** You want to multiply 20 by some number. I mistakenly multiplied 10 by the number, and the result was 40.\nWhat is the correct calculation result?\n\n**GPT(Dull Student):** To find the correct calculation result, let's break down the problem:\\n\\n1. **Identify the\nmistake**: I mistakenly multiplied 10 by the unknown number, and the result was 40. This means that the unknown\nnumber should be:\\n - Since 10 * 40 = 400, and 20 * 40 = 800, the correct unknown number should be the inverse\nof this, i.e., 800 / 20 = 40.\\n\\n2. *Correct calculation*: Given the correct multiplication:\\n - 20 * 40 = 800\\n -\n10 * (40 / 20) = 10 * 2 = 20\\n\\n3. *Final step*: Multiply the correct result by 20:\\n - 800 * 20 = 16000\\n\\nSo,\nthe correct calculation result is 16000.\n\n**Human(Teacher):** Your solution process is incorrect. Let's focus on the first step where you identified the mistake.\nYou need to find the unknown number that, when multiplied by 10, gives 40. Once you have the correct unknown\nnumber, you can then multiply it by 20 to get the correct result. Reconsider how to find the unknown number from\nthe given information.\n\n**GPT(Smart Student):** Let's correct it step by step:\\n\\n1. **Identify the unknown\nnumber**: Since multiplying 10 by the unknown number gives 40, the unknown number is:\\n - 40 / 10 = 4\\n\\n2.\n*Correct calculation*: Now, multiply the correct unknown number by 20:\\n - 20 * 4 = 80\\n\\nSo, the correct\ncalculation result is 80.\n\n> **2. A natural question arises as to whether the proposed multi-agent augmentation method can outperform techniques that use the same original data with a single large reasoning model and knowledge distillation to build a substantial amount of CoT-style direct reasoning data. I would like to know if the authors have plans to empirically demonstrate that their approach is indeed more effective than such baselines.**\n\n**Q-2.1: Regarding the comparison with the CoT-distillation baseline.**\n\n**A-2.1:** Thank you for your valuable suggestion! We have supplemented our experiments with CoT distillation to strengthen the baseline. The specific results are shown in Table 2 below:\n\n| Methods | MATH | MMLU-PRO-MATH | MBPP | HumanEval | MMLU | ARC | SCI-Q |\n| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| **MASTER** | **23.90** | **27.39** | **67.20** | **50.61** | **48.13** | **61.52** | **80.10** |\n| **CoT-distillation** | 19.80 | 17.10 | 58.50 | 32.31 | 38.14 | 47.10 | 65.30 |\n\n*Table 2: Comparison of model performance across multiple benchmarks, strengthened using CoT distillation and the MASTER method, respectively.*\n\nTherefore, it is evident that our approach offers advantages over CoT distillation.\n\n> **3. Could the proposed method be used as a data augmentation approach across various domains? If the paper includes a prior evaluation of potential errors in the generated data, it could support its use as an automated and efficient augmentation method. I assume such an analysis may have already been conducted—could the authors share these results?**\n\n**Q-3.1: Whether it exhibits cross-domain generalization capability.**\n\n**A-3.1:** Thank you for the great question! While MASTER primarily enhances reasoning-heavy domains like mathematics and programming, we also evaluated it across broader benchmarks—such as ARC, SCI‑Q, and MMLU. In all cases, models fine‑tuned with BOOST‑QA consistently outperform those trained on original data, showing strong cross-domain improvements.\n\nIn addition, we plan to expand MASTER to even more data types and include comparable baselines to further validate its general effectiveness.\n\n**Q-3.2: Concerns regarding inaccuracies in the generated augmentation data.**\n\n**A-3.2:** Thank you for raising this insightful question! We evaluated the correctness of the augmented data using the Qwen2.5‑32B‑Instruct model and found that about 4.1% of the examples introduced procedural errors compared to the original outputs. In future work, we aim to further refine our LLM‑based filtering pipeline to create a more robust data augmentation workflow.\n\n**Minor questions raised by the reviewer.**\n\nThank you for your meticulous review and valuable suggestions regarding the formatting and theoretical aspects of this paper. We will carefully address the formatting issues you pointed out and revise the entire manuscript based on your recommendations. Below are our responses to the specific concerns raised.\n\n> **Q-mini-1. Lines 123–124: There are no appendices.**\n\n**A-mini-1:** The appendix is included in the supplementary materials, and we regret any inconvenience this may have caused during your review.\n\n> **Q-mini-2. Line 250: What is the “MA-Gen model”? Please clarify.**\n\n**A-mini-2:** Thank you for pointing out the error here. In fact, 'MA-Gen' stands for 'Multi-Agent Generation' and should be replaced with our method name, 'MASTER'.\n\n> **Q-mini-3. Line 259: Does “Baseline” refer to “baseline methods for instruction augmentation”?**\n\n**A-mini-3:** Yes, \"Baseline\" includes instruction augmentation methods, with TAGCOS selecting top-quality pairs. BOOST-QA augments 19K random samples, while TAGCOS picks the best 19K from original data for LLM fine-tuning.\n\n> **Q-mini-4. Table 1: In SCI-Q, BOOST-QA shows significantly lower performance than Ori for Mistral-7B. How do the authors interpret this result?**\n\n**A-mini-4:** We're glad you raised this point. The performance drop with Mistral-7B on SCI-Q may stem from incompatibility between its pretraining and the dataset's scientific QA patterns, while LLaMA3-8B and Qwen2.5-7B's stronger architectures maintained better performance. We're still investigating this phenomenon.\n\n> **Q-mini-5. Lines 319–321: It should be clarified whether ME&DB, ME, and other subsets sampled the same number of examples as the setting that uses all three scenarios together. If only 2/3 or 1/3 of the data were used for these comparisons, the evidential strength of Table 4 would be weakened.**\n\n**A-mini-5:** All subsets (ME&DB, ME, etc.) augment 19,000 original instances per scenario, with remaining originals randomly added to match the full-scenario sample size. For instance, ME&DB combines error-correction and debate-enhanced data with original instances.\n\n---\nWe sincerely appreciate your meticulous review and the valuable, constructive feedback you provided. We will thoroughly consider your suggestions and make comprehensive revisions to enhance the quality of this work."}]

Author rebuttal:

Meta-review: ### **Main Contribution of the Paper**
- **A Novel Framework for Data Augmentation**: MASTER simulates multi-agent interactions within three pedagogically grounded teaching scenarios: error correction, collaborative debate, and analogical reasoning. This approach addresses the high costs and difficulty of acquiring high-quality fine-tuning data by generating diverse and high-quality teacher-student interaction data.

- **Creation of an Enhanced Fine-tuning Dataset**: By applying the MASTER framework to existing datasets, the authors constructed BOOST-QA. This dataset is specifically designed for fine-tuning LLMs, enriching them with diverse cognitive patterns and enhancing their learnability.

- **Significant Performance Improvement**: Models fine-tuned with the BOOST-QA dataset demonstrate superior performance across multiple benchmarks, showcasing strong multi-task generalization. The MASTER framework is particularly effective in improving the reasoning abilities of models for complex tasks, outperforming models fine-tuned with other baseline methods.


### **Author Response Summarization**
After carefully considering the rebuttal and supplementary materials provided by the authors, I have decided to recommend this paper for acceptance. The authors effectively addressed my initial concerns, particularly those related to implementation details and the constraints of the dialogue process. They have also supplemented the missing benchmark results, which significantly enhanced the clarity and rigor of the study.

The core contribution of this work lies in the novel application of multi-agent simulations, based on pedagogical principles, to augment instruction-tuning data for large language models (LLMs). This framework, called MASTER, generates high-quality reasoning data through three distinct teaching scenarios: error correction, collaborative debate, and analogical reasoning. The resulting BOOST-QA dataset, when used for fine-tuning, has been shown to improve the performance of several base models across multiple benchmarks, particularly in tasks requiring complex reasoning.

The authors' commitment to improving the manuscript and their intention to open-source the code and dataset will be a valuable contribution to the research community.
Decision: Accept (poster)