We present GoLongRL, a fully open-source, capability-oriented post-training recipe for long-context reinforcement learning with verifiable rewards (RLVR). Overall performance comparison on long-context benchmarks (DocMath, LongBench-V2, Frames, MRCR, CorpusQA, LBV1-QA). GoLongRL-4B achieves strong long-context performance at the 4B 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 (EM, Accuracy, F1, math_verify, IoU, SubEM, NDCG, Pairwise, ROUGE-L) 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. It provides a modest but consistent improvement over vanilla GRPO. 3. Full Open Release. We publicly…
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Автор: Kwai-Klear
Теги: qwen3, long-context, reinforcement-learning, rlvr, grpo, multitask, conversational, text-generation-inference
Лайков: 4 | Загрузок: 36
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
