PolicyLong: Towards On-Policy Context Extension

Abstract:Extending LLM context windows is hindered by scarce high-quality long-context data. Recent methods synthesize data with genuine long-range dependencies via information-theoretic verification, selecting contexts that reduce a base model's predictive entropy. However, their single-pass offline construction with a fixed model creates a fundamental off-policy gap: the static screening landscape misaligns with the model's evolving capabilities, causing the training distribution to drift. We propose PolicyLong, shifting data construction towards a dynamic on-policy paradigm. By iteratively re-executing data screening (entropy computation, retrieval, and verification) using the current model, PolicyLong ensures the training distribution tracks evolving capabilities, yielding an emergent self-curriculum. Crucially, both positive and hard negative contexts derive from the current model's entropy landscape, co-evolving what the model learns to exploit and resist. Experiments on RULER, HELMET, and LongBench-v2 (Qwen2.5-3B) show PolicyLong consistently outperforms EntropyLong and NExtLong, with gains growing at longer contexts (e.g., +2.54 at 128K on RULER), confirming the value of on-policy data evolution.

Work in progress. Correspondence to ucaswu@tencent.com or wuxing@iie.this http URL

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2604.07809 [cs.LG]

(or arXiv:2604.07809v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2604.07809 Focus to learn more arXiv-issued DOI via DataCite

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