📌 Overview

Overall performance comparison on long-context benchmarks (DocMath, LongBench-V2, Frames, MRCR, CorpusQA, LBV1-QA).

GoLongRL-30B-A3B achieves strong long-context performance at the 30B scale.

Our framework combines the following:

1. Capability-Oriented Dataset (23K samples, 9 task types). Guided by a taxonomy of long-context capabilities, the dataset covers precise retrieval, comprehension, exhaustive retrieval, numerical reasoning, structured extraction, structured matching, graded ranking, sequence ordering, and summarization. Each task is paired with its natural evaluation metric as the reward function.

2. TMN-Reweight. To address optimization challenges from heterogeneous rewards, we propose TMN-Reweight, which combines task-level mean normalization for cross-task reward scale alignment with difficulty-adaptive weighting for more reliable advantage estimation.

3. Full Open Release. We publicly release the complete dataset, the four-phase construction pipeline, and all training code.

Key Results

• Under the same vanilla GRPO setup, our dataset alone outperforms the closed-source QwenLong-L1.5 dataset at both 4B and 30B scales.
• TMN-Reweight further improves average performance over vanilla GRPO, with general capabilities preserved or improved across reported evaluations.
• Substantial gains on dialogue memory (LongMemEval +13.6) and agentic memory benchmarks.

🔍 Evaluation

Evaluation uses QwenLong-Benchmarks, covering three capability dimensions:

🤝 Citation

bibtex
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@misc{lv2026golongrlcapabilityorientedlongcontext,
      title={GoLongRL: Capability-Oriented Long Context Reinforcement Learning with Multitask Alignment}, 
      author={Minxuan Lv and Tiehua Mei and Tanlong Du and Junmin Chen and Zhenpeng Su and Ziyang Chen and Ziqi Wang and Zhennan Wu and Ruotong Pan and jian Liang and Ruiming Tang and Han Li},
      year={2026},
      eprint={2605.19577},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2605.19577}, 
}