Instruction Fine-Tuning (IFT) significantly enhances the zero-shot
capabilities of pretrained Large Language Models (LLMs). While coding data is
known to boost reasoning abilities during LLM pretraining, its role in
activating internal reasoning capacities during IFT remains understudied. This
paper investigates a key question: How does coding data impact LLMs' reasoning
capacities during the IFT stage? To explore this, we thoroughly examine the
impact of coding data across different coding data proportions, model families,
sizes, and reasoning domains, from various perspectives. Specifically, we
create three IFT datasets with increasing coding data proportions, fine-tune
six LLM backbones across different families and scales on these datasets,
evaluate the tuned models' performance across twelve tasks in three reasoning
domains, and analyze the outcomes from three broad-to-granular perspectives:
overall, domain-level, and task-specific. Our holistic analysis provides
valuable insights in each perspective. First, coding data tuning enhances the
overall reasoning capabilities of LLMs across different model families and
scales. Moreover, the effect of coding data varies among different domains but
shows consistent trends across model families and scales within each domain.
Additionally, coding data generally yields comparable task-specific benefits
across different model families, with the optimal coding data proportions in
IFT datasets being task-specific.

通过对编码数据在不同比例、模型家族和规模以及推理领域下对指导微调（IFT）的影响进行细致研究，本文发现编码数据的微调增强了大型语言模型（LLMs）在不同模型家族和规模上的整体推理能力，同时呈现了一致的趋势。

揭示编码数据指令微调对大型语言模型推理能力的影响

Unveiling the Impact of Coding Data Instruction Fine-Tuning on Large  Language Models Reasoning

Large language models (LLMs) have demonstrated impressive reasoning
capabilities, yet there is ongoing debate about these abilities and the
potential data contamination problem recently. This paper aims to evaluate the
reasoning capacities of LLMs, specifically in solving recent competition-level
programming problems in Codeforces, which are expert-crafted and unique,
requiring deep understanding and robust reasoning skills. We first provide a
comprehensive evaluation of GPT-4's peiceived zero-shot performance on this
task, considering various aspects such as problems' release time, difficulties,
and types of errors encountered. Surprisingly, the peiceived performance of
GPT-4 has experienced a cliff like decline in problems after September 2021
consistently across all the difficulties and types of problems, which shows the
potential data contamination, as well as the challenges for any existing LLM to
solve unseen complex reasoning problems. We further explore various approaches
such as fine-tuning, Chain-of-Thought prompting and problem description
simplification, unfortunately none of them is able to consistently mitigate the
challenges. Through our work, we emphasis the importance of this excellent data
source for assessing the genuine reasoning capabilities of LLMs, and foster the
development of LLMs with stronger reasoning abilities and better generalization
in the future.

评估大语言模型在 Codeforces 编程问题上的推理能力，发现了潜在的数据污染问题，并探索了多种方法来解决这些挑战，强调了评估 LLMs 真正推理能力的重要性和未来更强大推理能力和更好泛化性能的发展。