title: OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks
    with Reinforcement Fine-Tuning
summary: OpenAI's recent introduction of Reinforcement Fine-Tuning (RFT) showcases the
  potential of reasoning foundation model and offers a new paradigm for
  fine-tuning beyond simple pattern imitation. This technical report presents
  OpenRFT, our attempt to fine-tune generalist reasoning models for
  domain-specific tasks under the same settings as RFT. OpenRFT addresses two key
  challenges of lacking reasoning step data and the limited quantity of training
  samples, by leveraging the domain-specific samples in three ways: question
  augmentation, synthesizing reasoning-process data, and few-shot ICL. The
  evaluation is conducted on SciKnowEval, where OpenRFT achieves notable
  performance gains with only 100 domain-specific samples for each task. More
  experimental results will be updated continuously in later versions. Source
  codes, datasets, and models are disclosed at:
  https://github.com/ADaM-BJTU/OpenRFT
paper_authors._id: 676a85a619a646a97e21e8e5
paper_authors.name: Yuxiang Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-30T19:34:36.448Z
paper_authors._id: 645b4a2978730bcc103dfe4d
paper_authors.avatarUrl: /avatars/de544de899897fd0a83506ff287123bc.svg
paper_authors.fullname: Yuxiang Zhang
paper_authors.type: user
paper_authors.user: TokerZ
paper_authors._id: 676a85a619a646a97e21e8e6
paper_authors.name: Yuqi Yang
paper_authors._id: 676a85a619a646a97e21e8e7
paper_authors.name: Jiangming Shu
paper_authors._id: 676a85a619a646a97e21e8e8
paper_authors.name: Yuhang Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-14T08:30:16.733Z
paper_authors._id: 6492a388bb63613026a34ccd
paper_authors.avatarUrl: /avatars/83352981ef3858db3655826a869ba730.svg
paper_authors.fullname: Yuhang Wang
paper_authors.type: user
paper_authors.user: Rykeryh
paper_authors._id: 676a85a619a646a97e21e8e9
paper_authors.name: Jinlin Xiao
paper_authors._id: 676a85a619a646a97e21e8ea
paper_authors.name: Jitao Sang
paper_title: OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks
    with Reinforcement Fine-Tuning
paper_summary: OpenAI's recent introduction of Reinforcement Fine-Tuning (RFT) showcases the
  potential of reasoning foundation model and offers a new paradigm for
  fine-tuning beyond simple pattern imitation. This technical report presents
  OpenRFT, our attempt to fine-tune generalist reasoning models for
  domain-specific tasks under the same settings as RFT. OpenRFT addresses two key
  challenges of lacking reasoning step data and the limited quantity of training
  samples, by leveraging the domain-specific samples in three ways: question
  augmentation, synthesizing reasoning-process data, and few-shot ICL. The
  evaluation is conducted on SciKnowEval, where OpenRFT achieves notable
  performance gains with only 100 domain-specific samples for each task. More
  experimental results will be updated continuously in later versions. Source
  codes, datasets, and models are disclosed at:
  https://github.com/ADaM-BJTU/OpenRFT
paper_ai_keywords: Reinforcement Fine-Tuning (RFT) generalist reasoning models domain-specific tasks question augmentation synthesizing reasoning-process data few-shot ICL SciKnowEval