LLMs Can Easily Learn to Reason from Demonstrations Structure, not
  content, is what matters!
Large reasoning models (LRMs) tackle complex reasoning problems by following
long chain-of-thoughts (Long CoT) that incorporate reflection, backtracking,
and self-validation. However, the training techniques and data requirements to
elicit Long CoT remain poorly understood. In this work, we find that a Large
Language model (LLM) can effectively learn Long CoT reasoning through
data-efficient supervised fine-tuning (SFT) and parameter-efficient low-rank
adaptation (LoRA). With just 17k long CoT training samples, the
Qwen2.5-32B-Instruct model achieves significant improvements on a wide range of
math and coding benchmarks, including 56.7% (+40.0%) on AIME 2024 and 57.0%
(+8.1%) on LiveCodeBench, competitive to the proprietary o1-preview model's
score of 44.6% and 59.1%. More importantly, we find that the structure of Long
CoT is critical to the learning process, whereas the content of individual
reasoning steps has minimal impact. Perturbations affecting content, such as
training on incorrect samples or removing reasoning keywords, have little
impact on performance. In contrast, structural modifications that disrupt
logical consistency in the Long CoT, such as shuffling or deleting reasoning
steps, significantly degrade accuracy. For example, a model trained on Long CoT
samples with incorrect answers still achieves only 3.2% lower accuracy compared
to training with fully correct samples. These insights deepen our understanding
of how to elicit reasoning capabilities in LLMs and highlight key
considerations for efficiently training the next generation of reasoning
models. This is the academic paper of our previous released Sky-T1-32B-Preview
model. Codes are available at https://github.com/NovaSky-AI/SkyThought.
67ac1c6436464325ebe3c6e3
Dacheng Li
claimed_verified
2025-02-27T09:18:06.131Z
63715b25ffc0489ed7d1f415
https://cdn-avatars.huggingface.co/v1/production/uploads/63715b25ffc0489ed7d1f415/xZJepbs0LRqFbW1knnBKR.jpeg
Dacheng Li
user
DachengLi
67ac1c6436464325ebe3c6e4
Shiyi Cao
claimed_verified
2025-02-14T08:01:33.967Z
64ebbae6895a36ab28de811a
https://cdn-avatars.huggingface.co/v1/production/uploads/64ebbae6895a36ab28de811a/gBiaQP4paS4L13eu-yRm7.jpeg
Shiyi Cao
user
eva98
67ac1c6436464325ebe3c6e5
Tyler Griggs
67ac1c6436464325ebe3c6e6
Shu Liu
67ac1c6436464325ebe3c6e7
Xiangxi Mo
67ac1c6436464325ebe3c6e8
Shishir G. Patil
67ac1c6436464325ebe3c6e9
Matei Zaharia
67ac1c6436464325ebe3c6ea
Joseph E. Gonzalez
67ac1c6436464325ebe3c6eb
Ion Stoica
LLMs Can Easily Learn to Reason from Demonstrations Structure, not
  content, is what matters!
Large reasoning models (LRMs) tackle complex reasoning problems by following
long chain-of-thoughts (Long CoT) that incorporate reflection, backtracking,
and self-validation. However, the training techniques and data requirements to
elicit Long CoT remain poorly understood. In this work, we find that a Large
Language model (LLM) can effectively learn Long CoT reasoning through
data-efficient supervised fine-tuning (SFT) and parameter-efficient low-rank
adaptation (LoRA). With just 17k long CoT training samples, the
Qwen2.5-32B-Instruct model achieves significant improvements on a wide range of
math and coding benchmarks, including 56.7% (+40.0%) on AIME 2024 and 57.0%
(+8.1%) on LiveCodeBench, competitive to the proprietary o1-preview model's
score of 44.6% and 59.1%. More importantly, we find that the structure of Long
CoT is critical to the learning process, whereas the content of individual
reasoning steps has minimal impact. Perturbations affecting content, such as
training on incorrect samples or removing reasoning keywords, have little
impact on performance. In contrast, structural modifications that disrupt
logical consistency in the Long CoT, such as shuffling or deleting reasoning
steps, significantly degrade accuracy. For example, a model trained on Long CoT
samples with incorrect answers still achieves only 3.2% lower accuracy compared
to training with fully correct samples. These insights deepen our understanding
of how to elicit reasoning capabilities in LLMs and highlight key
considerations for efficiently training the next generation of reasoning
models. This is the academic paper of our previous released Sky-T1-32B-Preview
model. Codes are available at https://github.com/NovaSky-AI/SkyThought.
Data-efficient supervised fine-tuning and parameter-efficient low-rank adaptation enable large language models to learn long chain-of-thought reasoning effectively, with structural correctness being more crucial than content accuracy in training samples.
Large reasoning models long chain-of-thoughts Large Language model data-efficient supervised fine-tuning parameter-efficient low-rank adaptation Qwen2.5-32B-Instruct model AIME 2024 LiveCodeBench Sky-T1-32B-Preview model