QwenLong-L1: Towards Long-Context Large Reasoning Models with
  Reinforcement Learning
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.
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Fanqi Wan
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2025-05-26T08:09:44.880Z
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Fanqi Wan
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Weizhou Shen
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2025-05-26T14:12:42.577Z
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Weizhou Shen
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Shengyi Liao
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2025-05-26T14:12:56.397Z
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LiaoShengyi
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Yingcheng Shi
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2025-05-27T07:55:05.457Z
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shiyingcheng
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Chenliang Li
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2025-05-26T14:13:23.931Z
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li
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chenliangli
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Ziyi Yang
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2025-05-26T14:13:43.167Z
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ZiYi Yang
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Ji Zhang
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Fei Huang
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Jingren Zhou
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2025-05-26T14:14:03.511Z
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Zhou
user
Jingren
6833d7c5a3262d6b1e4d3597
Ming Yan
QwenLong-L1: Towards Long-Context Large Reasoning Models with
  Reinforcement Learning
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.
A framework called QwenLong-L1 enhances large reasoning models for long-context reasoning through reinforcement learning, achieving leading performance on document question-answering benchmarks.
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