Which Long Documents Matter? A Controlled Study of Data Selection for Context Extension
Natural long documents are widely used for context extension, but which selection pipelines are effective remains unclear. We conduct a controlled comparison on Qwen2.5-3B at 128K under matched 6B-token continued-training budgets. Random sampling of natural long documents (random-long) outperforms repacking at longer evaluation lengths. Within the same candidate pool, selecting documents with high LongPPL key-token density yields further gains, unlike the evaluated generic-quality and LLM-judge selectors. We introduce truncLongPPL to reduce scoring cost by computing key-token density over a truncated prefix. At 128K, scoring the first half of the reference span yields downstream performan...
| Authors | Junlong Jia, Jiang Zhou, Xing Wu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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