GoLongRL: Capability-Oriented Long Context Reinforcement Learning with Multitask Alignment
We present GoLongRL, a fully open-source, capability-oriented post-training recipe for long-context reinforcement learning with verifiable rewards (RLVR). Existing long-context RL methods often treat data construction as a matter of designing increasingly complex retrieval paths, leading to homogeneous task coverage and reward formulations that inadequately reflect practical long-context requirements. Our work offers two contributions. (1) Capability-oriented data construction with full open release. We openly release a dataset of 23K RLVR samples, the complete construction pipeline, and all training code. Guided by a taxonomy of long-context capabilities, the dataset spans 9 task types, ...
| Authors | Minxuan Lv, Tiehua Mei, Tanlong Du et al. |
|---|---|
| Primary category | cs.CL |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
Connected in the knowledge graph
- Kwai-Klear/GoLongRLDataset · cited by
- Kwai-Klear/GoLongRL-30B-A3BModel · cited by
Models and datasets referencing this paper
- Kwai-Klear/GoLongRLDataset
- Kwai-Klear/GoLongRL-30B-A3BModel
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