A Technical Study into Small Reasoning Language Models
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.
68516d328a68fee7f6ba4ccc
Xialie Zhuang
claimed_verified
2025-06-17T15:13:08.213Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/6575a625b951d40e7a4d8685/mcnyqU--r-vi11aXN02cZ.jpeg
Lie
user
Lie24
68516d328a68fee7f6ba4ccd
Peixian Ma
claimed_verified
2025-06-17T15:13:06.442Z
663e29ddc0ce43e121c80f92
https://cdn-avatars.huggingface.co/v1/production/uploads/663e29ddc0ce43e121c80f92/jEAQa2oLCRUpiFEhTy0nK.jpeg
Peixian Ma
user
MPX0222forHF
68516d328a68fee7f6ba4cce
Zhikai Jia
claimed_verified
2025-06-18T12:16:46.504Z
66ab85b1cd26e168a58333d7
/avatars/0bde87e64700aab178fae99120399a9d.svg
Zhikai Jia
user
jazka
68516d328a68fee7f6ba4ccf
Zheng Cao
68516d328a68fee7f6ba4cd0
Shiwei Liu
A Technical Study into Small Reasoning Language Models
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.
The research explores training strategies such as supervised fine-tuning, knowledge distillation, and reinforcement learning to enhance the performance of resource-efficient Small Reasoning Language Models with limited capacity.
supervised fine-tuning knowledge distillation reinforcement learning