Papers with LLM pretraining
Beyond Random Sampling: Efficient Language Model Pretraining via Curriculum Learning (2026.eacl-long)
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| Challenge: | Curriculum learning has improved efficiency across machine learning domains, but remains underexplored for language model pretraining. |
| Approach: | They present a systematic investigation of curriculum learning in LLM pretraining . they use vanilla curriculum learning, pacing-based sampling, and interleaved curricula . |
| Outcome: | The proposed framework accelerates convergence in early and mid-training phases, reducing training steps by 18-45% to reach baseline performance. |
Tag&Tab: Pretraining Data Detection in Large Language Models Using Keyword-Based Membership Inference Attack (2025.findings-emnlp)
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| Challenge: | Recent studies on detecting pretraining data in large language models have focused on sentence-level membership inference attacks (MIAs) but these methods often exhibit poor accuracy, failing to account for the semantic importance of textual content and word significance. |
| Approach: | They propose a method that leverages established natural language processing techniques to tag keywords in input text and then uses them to obtain probabilities and calculate their average log-likelihood to determine input text membership. |
| Outcome: | The proposed method exploits established natural language processing techniques to tag keywords in input text and calculate their average log-likelihood to determine input text membership. |
Does Data Contamination Detection Work (Well) for LLMs? A Survey and Evaluation on Detection Assumptions (2025.findings-naacl)
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| Challenge: | Large language models have demonstrated great performance across various benchmarks, but data contamination is a concern in their evaluation. |
| Approach: | They analyze 50 papers on data contamination detection and test three of them as case studies to identify the possibility of data contamination. |
| Outcome: | The proposed methods can detect membership inference attacks on instance-level data, and can perform similar to random guessing on LLM pretraining datasets. |
ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition (2026.findings-acl)
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Yujie Liu, Zonglin Yang, Tong Xie, Jinjie Ni, Ben Gao, Yuqiang Li, Shixiang Tang, Wanli Ouyang, Erik Cambria, Dongzhan Zhou
| Challenge: | Large language models have shown potential in assisting scientific research, yet their ability to discover high-quality research hypotheses remains unexamined due to the lack of a dedicated benchmark. |
| Approach: | They propose a benchmark for evaluating large language models on a sufficient set of scientific discovery sub-tasks. |
| Outcome: | The proposed framework extracts critical components from papers across 12 disciplines with expert validation confirming its accuracy. |
Craw4LLM: Efficient Web Crawling for LLM Pretraining (2025.findings-acl)
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| Challenge: | Existing work discards over 90% of the raw data collected from web crawls, highlighting the inefficiency of current web crawlers in collecting LLM pretraining data. |
| Approach: | They propose a web crawling method that leverages the preference of LLMs as the priority score of the web crawler’s scheduler to obtain high-quality pretraining data. |
| Outcome: | The proposed method achieves high-quality pretraining data on a web graph containing 900 million webpages from a commercial search engine's index with just 21% URLs crawled. |
Proving membership in LLM pretraining data via data watermarks (2024.findings-acl)
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| Challenge: | Detecting whether copyright holders’ works were used in large language model (LLM) training is poised to be an important problem. |
| Approach: | They propose to use data watermarks to enable principled detection with only black-box model access, provided the rightholder contributed multiple training documents and watermarked them before public release. |
| Outcome: | The proposed method can be used to test hypothesis testing on a black-box model . it shows that the watermarks are strong under model and dataset scaling . |
SAGE: Sign-Adaptive Gradient for Memory-Efficient LLM Optimization (2026.findings-acl)
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| Challenge: | Existing methods to train LLMs consume memory equivalent to twice the model size, resulting in a hybrid design that reverts to AdamW and negates the memory gains. |
| Approach: | They propose a new, memory-efficient O(d) adaptive scale that replaces AdamW in a hybrid structure that combines a Lion-style update direction with a memory-saving adaptive scale. |
| Outcome: | The proposed model outperforms existing methods on LLMs up to 1.3B parameters while significantly reducing optimizer state memory. |
Temporal Scaling Law for Large Language Models (2025.emnlp-main)
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Yizhe Xiong, Xiansheng Chen, Xin Ye, Hui Chen, Zijia Lin, Haoran Lian, Zhenpeng Su, Wei Huang, Jianwei Niu, Jungong Han, Guiguang Ding
| Challenge: | Existing studies have found that the test loss of LLMs scales as power-laws with model size, computational budget, and dataset size. |
| Approach: | They propose a concept of Temporal Scaling Law to study test loss of LLMs . they break down test loss into fine-grained token positions and develop a dynamic hyperbolic-law . |
| Outcome: | The proposed model predicts the test loss of LLMs as the training steps scale up. |
HTMuon: Improving Muon via Heavy-Tailed Spectral Correction (2026.findings-acl)
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| Challenge: | Muon’s orthogonalized update rule suppresses the emergence of heavy-tailed weight spectra and over-emphasizes the training along noise-dominated directions. |
| Approach: | They propose to preserve Muon's ability to capture parameter interdependencies while producing heavier-tailed updates and inducing heavier-tail weight spectra. |
| Outcome: | The proposed algorithm suppresses the emergence of heavy-tailed weight spectra and over-emphasizes training along noise-dominated directions. |