Beyond Scaling Law: A Data-Efficient Distillation Framework for Reasoning
Overview
Paper Summary
This paper proposes a new framework (DED) for training smaller language models to perform complex reasoning tasks efficiently by learning from larger, more capable models using a smaller, carefully curated dataset. The framework considers teacher model selection, data compression and diversity to optimize the learning process and achieve state-of-the-art results on mathematical reasoning and code generation tasks with significantly less data than prior work. The analysis also revealed the token entropy as a new proxy metric of corpus quality, which greatly impact the distillation outcome.
Explain Like I'm Five
This paper introduces a new way to train smaller AI models to be better at reasoning tasks, like math and coding, by learning from bigger, smarter models using a small but carefully selected set of examples.
Possible Conflicts of Interest
Two of the authors are affiliated with ZTE, and three are affiliated with China Mobile, which could potentially bias the selection and evaluation of models. However, the authors use established benchmarks and compare with a range of models, including open-source ones, mitigating this concern to some extent.
Identified Limitations
Rating Explanation
This paper presents a novel and practical approach to data-efficient distillation for reasoning tasks. The methodology is well-described, and the results demonstrate significant performance improvements compared to existing methods, particularly in low-resource settings. The systematic analysis of different factors affecting distillation, such as teacher selection and corpus properties, provides valuable insights. Although there are some limitations regarding the generalization of the framework and theoretical grounding, the overall contribution is significant enough for a rating of 4.
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