title: A Strategic Coordination Framework of Small LLMs Matches Large LLMs in
    Data Synthesis
summary: While data synthesis and distillation are promising strategies to enhance
  small language models, current approaches heavily rely on Large Language Models
  (LLMs), which suffer from high computational costs, environmental inefficiency,
  and potential biases inherited from monolithic architectures. In contrast,
  smaller LLMs are more accessible and sustainable, but their individual
  capabilities often fall short in generating high-quality, diverse, and reliable
  data. Inspired by collaborative human processes (e.g., peer review), we propose
  a multiple small LLMs involved framework, GRA, that aggregates specialized
  roles across small LLMs to iterative refinement and quality control typically
  achieved by a single large LLM. In this collaborative framework, multiple small
  LLMs assume distinct roles-Generator, Reviewer, and Adjudicator-to simulate a
  peer-review-inspired data synthesis pipeline. The Generator proposes initial
  data samples, the Reviewer critiques their quality and diversity, and the
  Adjudicator resolves conflicts to finalize the output. By decomposing the
  synthesis process into specialized sub-tasks, collaborative small LLMs can
  achieve data-level parity with large LLM-based distillation. Through
  experiments across multiple benchmarks, we demonstrate that GRA-produced data
  matches or exceeds the quality of single large LLM outputs, e.g.,
  Qwen-2.5-72B-Instruct. Our results challenge the necessity of monolithic large
  models for high-quality data synthesis, advocating instead for strategic
  coordination of smaller agents. Our datasets, models, and code are publicly
  available at https://github.com/GX-XinGao/GRA.
paper_authors._id: 6801d3de81552de84a537dd5
paper_authors.name: Xin Gao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-18T09:38:50.872Z
paper_authors._id: 652f9a74c22d404ebfa9f51d
paper_authors.avatarUrl: /avatars/8959d312b7c4c28952d4a26bb67f82ea.svg
paper_authors.fullname: gaoxin
paper_authors.type: user
paper_authors.user: GX-XinGao
paper_authors._id: 6801d3de81552de84a537dd6
paper_authors.name: Qizhi Pei
paper_authors._id: 6801d3de81552de84a537dd7
paper_authors.name: Zinan Tang
paper_authors._id: 6801d3de81552de84a537dd8
paper_authors.name: Yu Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-29T08:00:11.140Z
paper_authors._id: 671b852aa4fa4f8f5fb5404c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/671b852aa4fa4f8f5fb5404c/TDLsgP8WgKW-qaA8Ys-iJ.jpeg
paper_authors.fullname: YU LI
paper_authors.type: user
paper_authors.user: yu0226
paper_authors._id: 6801d3de81552de84a537dd9
paper_authors.name: Honglin Lin
paper_authors._id: 6801d3de81552de84a537dda
paper_authors.name: Jiang Wu
paper_authors._id: 6801d3de81552de84a537ddb
paper_authors.name: Conghui He
paper_authors._id: 6801d3de81552de84a537ddc
paper_authors.name: Lijun Wu
paper_title: A Strategic Coordination Framework of Small LLMs Matches Large LLMs in
    Data Synthesis
paper_summary: While data synthesis and distillation are promising strategies to enhance
  small language models, current approaches heavily rely on Large Language Models
  (LLMs), which suffer from high computational costs, environmental inefficiency,
  and potential biases inherited from monolithic architectures. In contrast,
  smaller LLMs are more accessible and sustainable, but their individual
  capabilities often fall short in generating high-quality, diverse, and reliable
  data. Inspired by collaborative human processes (e.g., peer review), we propose
  a multiple small LLMs involved framework, GRA, that aggregates specialized
  roles across small LLMs to iterative refinement and quality control typically
  achieved by a single large LLM. In this collaborative framework, multiple small
  LLMs assume distinct roles-Generator, Reviewer, and Adjudicator-to simulate a
  peer-review-inspired data synthesis pipeline. The Generator proposes initial
  data samples, the Reviewer critiques their quality and diversity, and the
  Adjudicator resolves conflicts to finalize the output. By decomposing the
  synthesis process into specialized sub-tasks, collaborative small LLMs can
  achieve data-level parity with large LLM-based distillation. Through
  experiments across multiple benchmarks, we demonstrate that GRA-produced data
  matches or exceeds the quality of single large LLM outputs, e.g.,
  Qwen-2.5-72B-Instruct. Our results challenge the necessity of monolithic large
  models for high-quality data synthesis, advocating instead for strategic
  coordination of smaller agents. Our datasets, models, and code are publicly
  available at https://github.com/GX-XinGao/GRA.
paper_ai_summary: A collaborative framework using multiple small language models achieves high-quality data synthesis similar to large language models through a peer-review-inspired process.
paper_ai_keywords: small language models large language models (LLMs) data synthesis distillation monolithic architectures Generator Reviewer Adjudicator peer-review-inspired quality control data-level parity