---
title: "Who cites How to Train Long-Context — 78 citing papers"
description: "78 papers of the Elamur Research library cite «How to Train Long-Context Language Models (Effectively)», found in their bibliographies; the…"
canonical: "https://research.elamur.ai/free/en/papers/gao-2025-how-train-long-context-language/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [How to Train Long-Context Language Models…](https://md.research.elamur.ai/free/en/papers/gao-2025-how-train-long-context-language/) / cited by

# Who cites «How to Train Long-Context Language Models (Effectively)»

78 papers of our library cite it (of the 141,840 whose bibliographies we read) · 8 with a page here, the most cited first · [← back to «How to Train Long-Context Language Models (Effectively)»](https://md.research.elamur.ai/free/en/papers/gao-2025-how-train-long-context-language/)

1. [**Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference arXiv:2412.13663**Benjamin Warner, Antoine Chaffin, Benjamin Clavié, et al.*2024 · 355 citations*](https://md.research.elamur.ai/free/en/papers/warner-2024-smarter-better-faster-longer-modern/)
2. [**SmolLM2: When Smol Goes Big — Data-Centric Training of a Small Language Model arXiv:2502.02737**Loubna Ben Allal, Anton Lozhkov, Elie Bakouch, et al.*2025 · 171 citations*](https://md.research.elamur.ai/free/en/papers/allal-2025-smollm2-smol-goes-big-data/)
3. [**LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks arXiv:2412.15204**Yushi Bai, Shangqing Tu, Jiajie Zhang, et al.*2024 · 130 citations*](https://md.research.elamur.ai/free/en/papers/bai-2024-longbench-v2-deeper-understanding/)
4. [**Olmo 3 arXiv:2512.13961**Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.*2025 · 109 citations*](https://md.research.elamur.ai/free/en/papers/ettinger-2025-olmo-3/)
5. [**MagicPIG: LSH Sampling for Efficient LLM Generation arXiv:2410.16179**Zhuoming Chen, Ranajoy Sadhukhan, Zihao Ye, et al.*2024 · 66 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-magicpig-lsh-sampling-efficient-llm/)
6. [**A Comprehensive Survey on Long Context Language Modeling arXiv:2503.17407**Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-comprehensive-survey-long-context-language/)
7. [**mmBERT: A Modern Multilingual Encoder with Annealed Language Learning arXiv:2509.06888**Marc Marone, Orion Weller, William Fleshman, et al.*2025 · 59 citations*](https://md.research.elamur.ai/free/en/papers/marone-2025-mmbert-modern-multilingual-encoder-annealed/)
8. **[New Encoders for German Trained from Scratch: Comparing ModernGBERT with Converted LLM2Vec Models](https://md.research.elamur.ai/free/en/papers/wunderle-2026-new-encoders-german-trained-scratch/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Julia Wunderle, Anton Ehrmanntraut, Jan Pfister, et al.*2026 · 7 citations*

70 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/gao-2025-how-train-long-context-language/) — How to Train Long-Context Language Models (Effectively)