Beyond Scaling Law: A Data-Efficient Distillation Framework for Reasoning

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Paper Summary

Conflicts of Interest

Identified Weaknesses

Rating Explanation

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Paper Summary

Paperzilla title

Teaching Tiny AIs to Think Big: Data-Efficient Distillation for Reasoning

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.

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 Weaknesses

Dependence on specific base model

The results heavily rely on the performance of a base model (DS-32B), and it's unclear how generalizable the approach is to other base models or architectures.

Limited evaluation on diverse datasets

The training datasets are derived from specific benchmarks and teacher models, making it hard to assess the true generalization ability of the proposed framework on truly unseen data or diverse real-world tasks.

Lack of theoretical grounding for certain techniques

The paper introduces several heuristics for dataset compression and diversity, but doesn't provide a clear theoretical justification or rigorous analysis of their impact on the learning process.

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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