LLMs Can Easily Learn to Reason from Demonstrations Structure, not
  content, is what matters!
Authors: name: Dacheng Li, name: Shiyi Cao, name: Tyler Griggs, name: Shu Liu, name: Xiangxi Mo, name: Shishir G. Patil, name: Matei Zaharia, name: Joseph E. Gonzalez, name: Ion Stoica

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.