title: QwenLong-L1: Towards Long-Context Large Reasoning Models with
    Reinforcement Learning
summary: Recent large reasoning models (LRMs) have demonstrated strong reasoning
  capabilities through reinforcement learning (RL). These improvements have
  primarily been observed within the short-context reasoning tasks. In contrast,
  extending LRMs to effectively process and reason on long-context inputs via RL
  remains a critical unsolved challenge. To bridge this gap, we first formalize
  the paradigm of long-context reasoning RL, and identify key challenges in
  suboptimal training efficiency and unstable optimization process. To address
  these issues, we propose QwenLong-L1, a framework that adapts short-context
  LRMs to long-context scenarios via progressive context scaling. Specifically,
  we utilize a warm-up supervised fine-tuning (SFT) stage to establish a robust
  initial policy, followed by a curriculum-guided phased RL technique to
  stabilize the policy evolution, and enhanced with a difficulty-aware
  retrospective sampling strategy to incentivize the policy exploration.
  Experiments on seven long-context document question-answering benchmarks
  demonstrate that QwenLong-L1-32B outperforms flagship LRMs like OpenAI-o3-mini
  and Qwen3-235B-A22B, achieving performance on par with
  Claude-3.7-Sonnet-Thinking, demonstrating leading performance among
  state-of-the-art LRMs. This work advances the development of practical
  long-context LRMs capable of robust reasoning across information-intensive
  environments.
paper_authors._id: 6833d7c5a3262d6b1e4d358e
paper_authors.name: Fanqi Wan
paper_authors.status: claimed_verified
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paper_authors.fullname: Fanqi Wan
paper_authors.type: user
paper_authors.user: Wanfq
paper_authors._id: 6833d7c5a3262d6b1e4d358f
paper_authors.name: Weizhou Shen
paper_authors.status: admin_assigned
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paper_authors.fullname: Weizhou Shen
paper_authors.type: user
paper_authors.user: shenwzh3
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paper_authors.name: Shengyi Liao
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paper_authors.fullname: LiaoShengyi
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paper_authors.user: LsyLsyLsyyy
paper_authors._id: 6833d7c5a3262d6b1e4d3591
paper_authors.name: Yingcheng Shi
paper_authors.status: claimed_verified
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paper_authors.fullname: shiyingcheng
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paper_authors.user: shiyingcheng
paper_authors._id: 6833d7c5a3262d6b1e4d3592
paper_authors.name: Chenliang Li
paper_authors.status: admin_assigned
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paper_authors.fullname: li
paper_authors.type: user
paper_authors.user: chenliangli
paper_authors._id: 6833d7c5a3262d6b1e4d3593
paper_authors.name: Ziyi Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-26T14:13:43.167Z
paper_authors._id: 64c9b0f28d2d187c24d1e6c1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/1CPnAaB3gsupdpiNWaoDc.png
paper_authors.fullname: ZiYi Yang
paper_authors.type: user
paper_authors.user: AALF
paper_authors._id: 6833d7c5a3262d6b1e4d3594
paper_authors.name: Ji Zhang
paper_authors._id: 6833d7c5a3262d6b1e4d3595
paper_authors.name: Fei Huang
paper_authors._id: 6833d7c5a3262d6b1e4d3596
paper_authors.name: Jingren Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-26T14:14:03.511Z
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paper_authors.fullname: Zhou
paper_authors.type: user
paper_authors.user: Jingren
paper_authors._id: 6833d7c5a3262d6b1e4d3597
paper_authors.name: Ming Yan
paper_title: QwenLong-L1: Towards Long-Context Large Reasoning Models with
    Reinforcement Learning
paper_summary: Recent large reasoning models (LRMs) have demonstrated strong reasoning
  capabilities through reinforcement learning (RL). These improvements have
  primarily been observed within the short-context reasoning tasks. In contrast,
  extending LRMs to effectively process and reason on long-context inputs via RL
  remains a critical unsolved challenge. To bridge this gap, we first formalize
  the paradigm of long-context reasoning RL, and identify key challenges in
  suboptimal training efficiency and unstable optimization process. To address
  these issues, we propose QwenLong-L1, a framework that adapts short-context
  LRMs to long-context scenarios via progressive context scaling. Specifically,
  we utilize a warm-up supervised fine-tuning (SFT) stage to establish a robust
  initial policy, followed by a curriculum-guided phased RL technique to
  stabilize the policy evolution, and enhanced with a difficulty-aware
  retrospective sampling strategy to incentivize the policy exploration.
  Experiments on seven long-context document question-answering benchmarks
  demonstrate that QwenLong-L1-32B outperforms flagship LRMs like OpenAI-o3-mini
  and Qwen3-235B-A22B, achieving performance on par with
  Claude-3.7-Sonnet-Thinking, demonstrating leading performance among
  state-of-the-art LRMs. This work advances the development of practical
  long-context LRMs capable of robust reasoning across information-intensive
  environments.
paper_ai_summary: A framework called QwenLong-L1 enhances large reasoning models for long-context reasoning through reinforcement learning, achieving leading performance on document question-answering benchmarks.
paper_ai_keywords: reinforcement learning long-context reasoning short-context reasoning training efficiency optimization process QwenLong-L1 progressive context scaling supervised fine-tuning curriculum-guided phased RL difficulty-aware retrospective sampling document question-answering benchmarks OpenAI-o3-mini Qwen3-235B-A22B Claude-3.7-Sonnet-Thinking