A framework called QwenLong-L1 enhances large reasoning models for long-context reasoning through reinforcement learning, achieving leading performance on document question-answering benchmarks.
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.
