title: One Missing Piece for Open-Source Reasoning Models: A Dataset to
    Mitigate Cold-Starting Short CoT LLMs in RL
summary: With the release of R1, a publicly available large reasoning model (LRM),
  researchers commonly train new LRMs by training language models on R1's long
  chain-of-thought (CoT) inferences. While prior works show that LRMs'
  capabilities can be reproduced through direct distillation, the continued
  reliance on the existing models (e.g., R1) remains a critical limitation in
  advancing the field. As a first step toward independent LRM development, this
  paper explores the possibility of constructing a long CoT dataset with LLMs
  that are not trained for inference-time scaling. To this end, we present the
  Long CoT Collection, a dataset of 100K CoT rationales annotated using existing
  short CoT LLMs. We develop a pipeline that induces o1's novel reasoning
  strategies into short CoT LLMs, enabling them to think longer and introducing
  controllability over the thought budget to better manage the overthinking
  problem. Our extensive analyses validate that our dataset achieves quality
  comparable to--or slightly below--R1. Furthermore, our experiments demonstrate
  that training on our dataset not only strengthens general reasoning skills, but
  also provides a strong foundation for reinforcement learning--models
  initialized on our data achieve 2-3x larger gains with RLVR.
paper_authors._id: 683fbc730ffa93c1611d513b
paper_authors.name: Hyungjoo Chae
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-04T08:55:14.169Z
paper_authors._id: 64c8f4cec547ed5243ebd0a8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64c8f4cec547ed5243ebd0a8/MiOH5YbMg8Gh9KYlQsLmX.jpeg
paper_authors.fullname: Hyungjoo Chae
paper_authors.type: user
paper_authors.user: hyungjoochae
paper_authors._id: 683fbc730ffa93c1611d513c
paper_authors.name: Dongjin Kang
paper_authors._id: 683fbc730ffa93c1611d513d
paper_authors.name: Jihyuk Kim
paper_authors._id: 683fbc730ffa93c1611d513e
paper_authors.name: Beong-woo Kwak
paper_authors._id: 683fbc730ffa93c1611d513f
paper_authors.name: Sunghyun Park
paper_authors._id: 683fbc730ffa93c1611d5140
paper_authors.name: Haeju Park
paper_authors._id: 683fbc730ffa93c1611d5141
paper_authors.name: Jinyoung Yeo
paper_authors._id: 683fbc730ffa93c1611d5142
paper_authors.name: Moontae Lee
paper_authors._id: 683fbc730ffa93c1611d5143
paper_authors.name: Kyungjae Lee
paper_title: One Missing Piece for Open-Source Reasoning Models: A Dataset to
    Mitigate Cold-Starting Short CoT LLMs in RL
paper_summary: With the release of R1, a publicly available large reasoning model (LRM),
  researchers commonly train new LRMs by training language models on R1's long
  chain-of-thought (CoT) inferences. While prior works show that LRMs'
  capabilities can be reproduced through direct distillation, the continued
  reliance on the existing models (e.g., R1) remains a critical limitation in
  advancing the field. As a first step toward independent LRM development, this
  paper explores the possibility of constructing a long CoT dataset with LLMs
  that are not trained for inference-time scaling. To this end, we present the
  Long CoT Collection, a dataset of 100K CoT rationales annotated using existing
  short CoT LLMs. We develop a pipeline that induces o1's novel reasoning
  strategies into short CoT LLMs, enabling them to think longer and introducing
  controllability over the thought budget to better manage the overthinking
  problem. Our extensive analyses validate that our dataset achieves quality
  comparable to--or slightly below--R1. Furthermore, our experiments demonstrate
  that training on our dataset not only strengthens general reasoning skills, but
  also provides a strong foundation for reinforcement learning--models
  initialized on our data achieve 2-3x larger gains with RLVR.
paper_ai_summary: The Long CoT Collection dataset, generated by short CoT LLMs, enhances general reasoning skills and provides a strong foundation for reinforcement learning, achieving quality comparable to R1.
paper_ai_keywords: long chain-of-thought CoT inferences LRMs direct distillation inference-time scaling CoT rationales short CoT LLMs reasoning strategies thought budget overthinking reinforcement learning RLVR