title: FastCuRL: Curriculum Reinforcement Learning with Progressive Context
    Extension for Efficient Training R1-like Reasoning Models
summary: In this paper, we propose \textsc{FastCuRL}, a simple yet efficient
  Curriculum Reinforcement Learning approach with
  context window extending strategy to accelerate the reinforcement learning
  training efficiency for R1-like reasoning models while enhancing their
  performance in tackling complex reasoning tasks with long chain-of-thought
  rationales, particularly with a 1.5B parameter language model.
  \textsc{FastCuRL} consists of two main procedures: length-aware
  training data segmentation and context window extension training. Specifically,
  the former first splits the original training data into three different levels
  by the input prompt length, and then the latter leverages segmented training
  datasets with a progressively increasing context window length to train the
  reasoning model. Experimental results demonstrate that
  \textsc{FastCuRL}-1.5B-Preview surpasses DeepScaleR-1.5B-Preview
  across all five datasets (including MATH 500, AIME 2024, AMC 2023, Minerva
  Math, and OlympiadBench) while only utilizing 50\% of training steps.
  Furthermore, all training stages for FastCuRL-1.5B-Preview are completed using
  just a single node with 8 GPUs.
paper_authors._id: 67e0bfcc8fb92b0edaa78dc0
paper_authors.name: Mingyang Song
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-28T12:56:34.911Z
paper_authors._id: 617051728db4a760d912d81f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/617051728db4a760d912d81f/4dKG176GPk2pPRK7ctaT-.jpeg
paper_authors.fullname: Mingyang Song
paper_authors.type: user
paper_authors.user: Nickyang
paper_authors._id: 67e0bfcc8fb92b0edaa78dc1
paper_authors.name: Mao Zheng
paper_authors._id: 67e0bfcc8fb92b0edaa78dc2
paper_authors.name: Zheng Li
paper_authors._id: 67e0bfcc8fb92b0edaa78dc3
paper_authors.name: Wenjie Yang
paper_authors._id: 67e0bfcc8fb92b0edaa78dc4
paper_authors.name: Xuan Luo
paper_authors._id: 67e0bfcc8fb92b0edaa78dc5
paper_authors.name: Yue Pan
paper_authors._id: 67e0bfcc8fb92b0edaa78dc6
paper_authors.name: Feng Zhang
paper_title: FastCuRL: Curriculum Reinforcement Learning with Progressive Context
    Extension for Efficient Training R1-like Reasoning Models
paper_summary: In this paper, we propose \textsc{FastCuRL}, a simple yet efficient
  Curriculum Reinforcement Learning approach with
  context window extending strategy to accelerate the reinforcement learning
  training efficiency for R1-like reasoning models while enhancing their
  performance in tackling complex reasoning tasks with long chain-of-thought
  rationales, particularly with a 1.5B parameter language model.
  \textsc{FastCuRL} consists of two main procedures: length-aware
  training data segmentation and context window extension training. Specifically,
  the former first splits the original training data into three different levels
  by the input prompt length, and then the latter leverages segmented training
  datasets with a progressively increasing context window length to train the
  reasoning model. Experimental results demonstrate that
  \textsc{FastCuRL}-1.5B-Preview surpasses DeepScaleR-1.5B-Preview
  across all five datasets (including MATH 500, AIME 2024, AMC 2023, Minerva
  Math, and OlympiadBench) while only utilizing 50\% of training steps.
  Furthermore, all training stages for FastCuRL-1.5B-Preview are completed using
  just a single node with 8 GPUs.
paper_ai_summary: FastCuRL is a curriculum reinforcement learning method that accelerates training and improves performance on complex reasoning tasks using segmented data and progressively increased context windows.
paper_ai_keywords: Curriculum Reinforcement Learning context window length-aware training reasoning models chain-of-thought language model MATH 500 AIME 2024 AMC 2023 Minerva Math OlympiadBench