# Accelerating Large Language Model Pretraining via LFR Pedagogy: Learn, Focus, and Review

arXiv: https://arxiv.org/abs/2409.06131 · HTML: https://mlsanity.com/paper/2409.06131/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 10 Sep 2024.

## Builds on (most cited first, 16 of 16)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [The LAMBADA dataset: Word prediction requiring a broad discourse context](https://mlsanity.com/paper/1606.06031/) (2016)
- [The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only](https://mlsanity.com/paper/2306.01116/) (2023)
- [Deep Learning on a Data Diet: Finding Important Examples Early in Training](https://mlsanity.com/paper/2107.07075/) (2021)
- [Data Selection for Language Models via Importance Resampling](https://mlsanity.com/paper/2302.03169/) (2023)
- [One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling](https://mlsanity.com/paper/1312.3005/) (2013)
- [SemDeDup: Data-efficient learning at web-scale through semantic deduplication](https://mlsanity.com/paper/2303.09540/) (2023)
- [Unsupervised Neural Machine Translation](https://mlsanity.com/paper/1710.11041/) (2017)
- [Training Trajectories of Language Models Across Scales](https://mlsanity.com/paper/2212.09803/) (2022)
- [The MiniPile Challenge for Data-Efficient Language Models](https://mlsanity.com/paper/2304.08442/) (2023)
- [The right time to learn: mechanisms and optimization of spaced learning](https://mlsanity.com/paper/1606.08370/) (2016)

## Built on by (most cited first, 2 of 2)

- [LIFT: LLM-Based Pragma Insertion for HLS via GNN Supervised Fine-Tuning](https://mlsanity.com/paper/2504.21187/) (2025)
- [Do Syntactic Categories Help in Developmentally Motivated Curriculum Learning for Language Models?](https://mlsanity.com/paper/2511.08199/) (2025)

## Abstract (arXiv)

Traditional Large Language Model (LLM) pretraining relies on autoregressive language modeling with randomly sampled data from web-scale datasets. Inspired by human learning techniques like spaced repetition, we hypothesize that random sampling leads to high training costs, lower-quality models, and significant data forgetting. To address these inefficiencies, we propose the Learn-Focus-Review (LFR) paradigm -- a dynamic training approach that adapts to the model's learning progress. LFR tracks the model's learning performance across data blocks (sequences of tokens) and prioritizes revisiting challenging regions of the dataset that are more prone to being forgotten, enabling better retention and more efficient learning. Using the LFR paradigm, we pretrained Llama and GPT models on the SlimPajama and OpenWebText datasets, respectively. These models were evaluated on downstream tasks across various domains, including question answering, problem-solving, commonsense reasoning, language modeling, and translation. Compared to baseline models trained on the full datasets, LFR consistently achieved lower perplexity and higher accuracy, while using only 5%--19% of the training tokens. Furthermore, LFR matched the performance of industry-standard Pythia models with up to 2× the parameter count, using just 3.2% of the training tokens, demonstrating its effectiveness and efficiency.