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arXiv Machine Learning By Junlong Jia, Jiang Zhou, Ziyang Chen, Xing Wu, Chaochen Gao, TingHao Yu, Feng Zhang, Songlin Hu

PolicyLong: Towards On-Policy Context Extension

Read the original on arXiv Machine Learning →

PolicyLong introduces a dynamic on‑policy approach to constructing long‑context data for large language models, addressing the off‑policy gap of previous single‑pass methods. By repeatedly re‑screening data using the model’s current entropy landscape, it creates a self‑curriculum that aligns training distribution with evolving model capabilities. Experiments on RULER, HELMET, and LongBench‑v2 demonstrate consistent performance gains, especially at longer contexts, outperforming EntropyLong and NExtLong.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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