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

arXiv: https://arxiv.org/abs/2512.12967 · HTML: https://mlsanity.com/paper/2512.12967/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning.
- First posted on arXiv 15 Dec 2025.

## Placed in

- LLM reasoning / [Test-time compute and efficient thinking](https://mlsanity.com/lr/test-time-compute-and-efficient-thinking/)

## Built on by (most cited first, 27 of 27)

- [OPSDL: On-Policy Self-Distillation for Long-Context Language Models](https://mlsanity.com/paper/2604.17535/) (2026)
- [LongR: Unleashing Long-Context Reasoning via Reinforcement Learning with Dense Utility Rewards](https://mlsanity.com/paper/2602.05758/) (2026)
- [AgentLongBench: A Controllable Long Benchmark For Long-Contexts Agents via Environment Rollouts](https://mlsanity.com/paper/2601.20730/) (2026)
- [Dynamic Long Context Reasoning over Compressed Memory via End-to-End Reinforcement Learning](https://mlsanity.com/paper/2602.08382/) (2026)
- [Learning to Remember: End-to-End Training of Memory Agents for Long-Context Reasoning](https://mlsanity.com/paper/2602.18493/) (2026)
- [MAWARITH: A Dataset and Benchmark for Legal Inheritance Reasoning with LLMs](https://mlsanity.com/paper/2603.07539/) (2026)
- [GoLongRL: Capability-Oriented Long Context Reinforcement Learning with Multitask Alignment](https://mlsanity.com/paper/2605.19577/) (2026)
- [Towards Self-Improving Error Diagnosis in Multi-Agent Systems](https://mlsanity.com/paper/2604.17658/) (2026)
- [Intern-S2-Preview: Scientific Agentic Foundation Model](https://mlsanity.com/paper/2608.13505/) (2026)
- [Incentivizing In-depth Reasoning over Long Contexts with Process Advantage Shaping](https://mlsanity.com/paper/2601.12465/) (2026)
- [Group Entropy-Controlled Policy Optimization](https://mlsanity.com/paper/2607.16850/) (2026)
- [PETS: A Principled Framework Towards Optimal Trajectory Allocation for Efficient Test-Time Self-Consistency](https://mlsanity.com/paper/2602.16745/) (2026)
- [LongTraceRL: Learning Long-Context Reasoning from Search Agent Trajectories with Rubric Rewards](https://mlsanity.com/paper/2605.31584/) (2026)
- [LongVPO: From Anchored Cues to Self-Reasoning for Long-Form Video Preference Optimization](https://mlsanity.com/paper/2602.02341/) (2026)
- [Probing How Scalable Table Data Enhances General Long-Context Reasoning](https://mlsanity.com/paper/2603.21719/) (2026)
- [A Decomposition Perspective to Long-context Reasoning for LLMs](https://mlsanity.com/paper/2604.07981/) (2026)
- [InfoMem: Training Long-Context Memory Agents with Answer-Conditioned Information Gain](https://mlsanity.com/paper/2606.03329/) (2026)
- [Beyond Reward Engineering: A Data Recipe for Long-Context Reinforcement Learning](https://mlsanity.com/paper/2606.18831/) (2026)
- [Copy Less, Ground More: Overcoming Repetitive Copying in Long-Context Reasoning via Evidence-Aware Reinforcement Learning](https://mlsanity.com/paper/2607.19345/) (2026)
- [Understand and Accelerate Memory Processing Pipeline for Large Language Model Inference](https://mlsanity.com/paper/2603.29002/) (2026)
- [π2: Structure-Originated Reasoning Data Improves Long-Context Reasoning Ability of Large Language Models](https://mlsanity.com/paper/2604.05114/) (2026)
- [Storyline Trees: Hierarchical Representations for Long-Form Narratives](https://mlsanity.com/paper/2606.20900/) (2026)
- [PI-Mem: Pushing Long-Context Reasoning to 3.6M Tokens with Parallel-Iterative Memory](https://mlsanity.com/paper/2608.03048/) (2026)
- [Mint-Agent: Introducing Finance-Native Agentic Foundation Models](https://mlsanity.com/paper/2608.16386/) (2026)
- [TrajLong: Co-Designing Agentic and Long-Context Supervision for Mid-Training](https://mlsanity.com/paper/2610.04973/) (2026)
- [LongHarness: Advancing Agentic Reasoning over 100M-Token Corpora via Stateful Evidence Management](https://mlsanity.com/paper/2610.13075/) (2026)
- [MemDuet: Learning Dual-Process Memory for Long-Context Reasoning](https://mlsanity.com/paper/2610.19928/) (2026)

## Abstract (arXiv)

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.