A Strategic Coordination Framework of Small LLMs Matches Large LLMs in
  Data Synthesis
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.
6801d3de81552de84a537dd5
Xin Gao
claimed_verified
2025-04-18T09:38:50.872Z
652f9a74c22d404ebfa9f51d
/avatars/8959d312b7c4c28952d4a26bb67f82ea.svg
gaoxin
user
GX-XinGao
6801d3de81552de84a537dd6
Qizhi Pei
6801d3de81552de84a537dd7
Zinan Tang
claimed_verified
2025-07-15T19:12:36.247Z
66580d3d80ee5b1e11a94e57
https://cdn-avatars.huggingface.co/v1/production/uploads/66580d3d80ee5b1e11a94e57/aHaPqrV5vNefFktYRsiGf.jpeg
Zinan Tang
user
Word2Li
6801d3de81552de84a537dd8
Yu Li
claimed_verified
2025-04-29T08:00:11.140Z
671b852aa4fa4f8f5fb5404c
https://cdn-avatars.huggingface.co/v1/production/uploads/671b852aa4fa4f8f5fb5404c/TDLsgP8WgKW-qaA8Ys-iJ.jpeg
YU LI
user
yu0226
6801d3de81552de84a537dd9
Honglin Lin
6801d3de81552de84a537dda
Jiang Wu
6801d3de81552de84a537ddb
Conghui He
6801d3de81552de84a537ddc
Lijun Wu
A Strategic Coordination Framework of Small LLMs Matches Large LLMs in
  Data Synthesis
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.
A collaborative framework using multiple small language models achieves high-quality data synthesis similar to large language models through a peer-review-inspired process.
small language models large language models (LLMs) data synthesis distillation monolithic architectures Generator Reviewer Adjudicator peer-review-inspired quality control data-level parity