[Submitted on 17 Oct 2026]
Abstract: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 performance close to full-span scoring with a measured $2.28times$ end-to-end scoring speedup. Across three training seeds, truncLongPPL achieves the highest mean scores among evaluated pipelines, exceeding random-long by 4.54 points on validation HELMET and 3.28 on held-out RULER, with gains most consistent in retrieval. At a 256K target context, it gains 2.82 average RULER points over random-long on Qwen2.5-3B. In short/long mixed training across Qwen2.5-3B, Qwen3-8B, and OLMo~3-7B, it gains 1.47–7.31 average RULER points over random-long mixing, with seven-task short-context averages within 0.23 points of the corresponding baselines. After supervised fine-tuning (SFT), Qwen3-8B and OLMo~3-7B models trained with selected long documents outperform their random-long counterparts by 1.13–1.67 points on LongBench v2 and 0.62–1.44 points on MRCR.
Submission history
From: Wu Xing [view email]
[v1] Sat, 17 Oct 2026 05:29:37 UTC (1,300 KB)
