QwenLong-L1.5：面向长上下文推理与记忆管理的后训练优化方案

摘要

我们推出QwenLong-L1.5模型，该模型通过系统化的后训练创新实现了卓越的长上下文推理能力。其关键技术突破包括：（1）长上下文数据合成管道：开发了系统化合成框架，可生成需要基于全局分布证据进行多跳推理的挑战性任务。通过将文档解构为原子事实及其内在关联，再以可编程方式组合成可验证的推理问题，该方法能大规模生成高质量训练数据，显著超越简单检索任务，实现真正的长程推理能力；（2）长上下文训练的稳定强化学习：针对长上下文强化学习的不稳定性，提出任务平衡采样与任务特定优势估计以缓解奖励偏差，并设计自适应熵控策略优化（AEPO）动态调节探索-利用平衡；（3）超长上下文的记忆增强架构：针对扩展上下文窗口仍无法容纳无限长序列的难题，开发了具有多阶段融合强化训练的记忆管理框架，可对超过400万token的任务实现单次推理与基于记忆的迭代处理的无缝集成。基于Qwen3-300亿参数A3B思维架构的QwenLong-L1.5，在长上下文推理基准测试中达到与GPT-5和Gemini-2.5-Pro相当的水准，较基线模型平均提升9.90分。在超长任务（100万至400万token）上，其记忆智能体框架较智能体基线提升9.48分。此外，所获得的长上下文推理能力还显著提升了科学推理、记忆工具使用、长对话等通用领域的性能表现。

English

We introduce QwenLong-L1.5, a model that achieves superior long-context reasoning capabilities through systematic post-training innovations. The key technical breakthroughs of QwenLong-L1.5 are as follows: (1) Long-Context Data Synthesis Pipeline: We develop a systematic synthesis framework that generates challenging reasoning tasks requiring multi-hop grounding over globally distributed evidence. By deconstructing documents into atomic facts and their underlying relationships, and then programmatically composing verifiable reasoning questions, our approach creates high-quality training data at scale, moving substantially beyond simple retrieval tasks to enable genuine long-range reasoning capabilities. (2) Stabilized Reinforcement Learning for Long-Context Training: To overcome the critical instability in long-context RL, we introduce task-balanced sampling with task-specific advantage estimation to mitigate reward bias, and propose Adaptive Entropy-Controlled Policy Optimization (AEPO) that dynamically regulates exploration-exploitation trade-offs. (3) Memory-Augmented Architecture for Ultra-Long Contexts: Recognizing that even extended context windows cannot accommodate arbitrarily long sequences, we develop a memory management framework with multi-stage fusion RL training that seamlessly integrates single-pass reasoning with iterative memory-based processing for tasks exceeding 4M tokens. Based on Qwen3-30B-A3B-Thinking, QwenLong-L1.5 achieves performance comparable to GPT-5 and Gemini-2.5-Pro on long-context reasoning benchmarks, surpassing its baseline by 9.90 points on average. On ultra-long tasks (1M~4M tokens), QwenLong-L1.5's memory-agent framework yields a 9.48-point gain over the agent baseline. Additionally, the acquired long-context reasoning ability translates to enhanced performance in general domains like scientific reasoning, memory tool using, and extended dialogue.

QwenLong-L1.5：面向长上下文推理与记忆管理的后训练优化方案

QwenLong-L1.5: Post-Training Recipe for Long-Context Reasoning and Memory Management

摘要

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