A Technical Study into Small Reasoning Language Models
Authors: name: Xialie Zhuang, name: Peixian Ma, name: Zhikai Jia, name: Zheng Cao, name: Shiwei Liu

The ongoing evolution of language models has led to the development of
large-scale architectures that demonstrate exceptional performance across a
wide range of tasks. However, these models come with significant computational
and energy demands, as well as potential privacy implications. In this context,
Small Reasoning Language Models (SRLMs) with approximately 0.5 billion
parameters present a compelling alternative due to their remarkable
computational efficiency and cost effectiveness, particularly in
resource-constrained environments. Despite these advantages, the limited
capacity of 0.5 billion parameter models poses challenges in handling complex
tasks such as mathematical reasoning and code generation. This research
investigates various training strategies, including supervised fine-tuning
(SFT), knowledge distillation (KD), and reinforcement learning (RL), as well as
their hybrid implementations, to enhance the performance of 0.5B SRLMs. We
analyze effective methodologies to bridge the performance gap between SRLMS and
larger models and present insights into optimal training pipelines tailored for
these smaller architectures. Through extensive experimental validation and
analysis, our work aims to provide actionable recommendations for maximizing
the reasoning capabilities of 0.5B models.