mlsanityData to 7 Oct 2026

OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning

arXiv 2412.16849 ↗First posted 22 Dec 2024cs.AIcited by 13

On the map: LLM reasoning → Reinforcement learning for reasoning

Abstract (arXiv)

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

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Placed in Reinforcement learning for reasoning, one of 17 directions in LLM reasoning. Builds on 10 of its 19 references that are in the map. First posted on arXiv 22 Dec 2024.

Builds on

10 of 19 references.

Closest ancestors · 1

Same direction, newest first

  1. 2017Proximal Policy Optimization Algorithms20 Jul

Other references · 9

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2024Qwen2 Technical Reportcited by 2,076
  3. 2024ReFT: Reasoning with Reinforced Fine-Tuningcited by 236
  4. 2023Grounding Large Language Models in Interactive Environments with Online Reinforcement Learningcited by 193
  5. 2024OpenR: An Open Source Framework for Advanced Reasoning with Large Language Modelscited by 92
  6. 2024o1-Coder: an o1 Replication for Codingcited by 77
  7. 2017N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learningcited by 33
  8. 2021DECORE: Deep Compression with Reinforcement Learningcited by 29
  9. 2024ALR2: A Retrieve-then-Reason Framework for Long-context Question Answeringcited by 25

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 12; 2026: 1.

13 papers.

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  1. 2025Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Modelscited by 381
  2. 2025Crossing the Reward Bridge: Expanding RL with Verifiable Rewards Across Diverse Domainscited by 150
  3. 2025Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Modelscited by 134
  4. 2025Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoningcited by 101
  5. 2025Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Librarycited by 94
  6. 2025One Token to Fool LLM-as-a-Judgecited by 56
  7. 2025ReARTeR: Retrieval-Augmented Reasoning with Trustworthy Process Rewardingcited by 32
  8. 2025A Survey of Slow Thinking-based Reasoning LLMs using Reinforced Learning and Inference-time Scaling Lawcited by 18
  9. 2025Agent models: Internalizing Chain-of-Action Generation into Reasoning modelscited by 17
  10. 2026Save the Good Prefix: Precise Error Penalization via Process-Supervised RL to Enhance LLM Reasoningcited by 10
  11. 2025Beyond Pipelines: A Survey of the Paradigm Shift toward Model-Native Agentic AIcited by 8
  12. 2025Dynamic Chain-of-Thought: Towards Adaptive Deep Reasoningcited by 5
1 more
  1. 2025HAMMER: Hamiltonian Curiosity Augmented Large Language Model Reinforcementcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025When Actions Teach You to Think: Reasoning-Action Synergy via Reinforcement Learning in Conversational Agents
  2. 2026Native Reasoning Models: Training Language Models to Reason on Unverifiable Data
  3. 2025UFT: Unifying Supervised and Reinforcement Fine-Tuning
  4. 2025Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models
  5. 2026DeReason: A Difficulty-Aware Curriculum Improves Decoupled SFT-then-RL Training for General Reasoning
  6. 2026Rethinking Generalization in Reasoning SFT: A Conditional Analysis on Optimization, Data, and Model Capability
  7. 2025SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
  8. 2025Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest Questions