Core Viewpoint - The GRA framework (Generator–Reviewer–Adjudicator) proposed by Shanghai AI Lab and Renmin University of China enables small models to collaboratively generate high-quality training data without the need for large-scale language model distillation [1][2][13]. Group 1: GRA Framework Overview - GRA operates on the principle of "multi-person collaboration" and "role division," simulating a peer review process to ensure data quality [7][12]. - The framework consists of three main roles: Generator, Reviewer, and Adjudicator, each contributing to the data generation and evaluation process [8][9][10]. Group 2: Experimental Results - GRA-generated data quality matches or exceeds that of single large language models across ten mainstream datasets, showing significant performance improvements [2][14]. - The GRA framework integrates five open-source small language models, demonstrating that collaboration among smaller models can yield competitive results against larger models [14][17]. Group 3: Performance Metrics - GRA-generated data improved training performance by an average of 6.18% on LLaMA-3.1 and 11.81% on Qwen-2.5 compared to original data [16]. - GRA's performance is only 0.59% lower than the Qwen-72B distilled version, while outperforming it by 8.83% when trained on Qwen-2.5 data [17]. Group 4: Advantages of GRA - GRA enhances data diversity and quality, filling gaps in the original seed data and providing a broader semantic coverage [18]. - The data quality is validated through a robust review process, with over 87.3% of samples receiving high consistency scores [19]. - GRA-generated data presents a higher task difficulty, increasing the effectiveness of training for small models [20].