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1. Flextron: Many-in-One Flexible Large Language Model

   Cai, Ruisi; Muralidharan, Saurav; Heinrich, Greg; Yin, Hongxu; Wang, Zhangyang; Kautz, Jan; Molchanov, Pavlo (August 2024, https://doi.org/10.48550/arXiv.2406.10260)

   raining modern large language models (LLMs) is extremely resource-intensive, and repeatedly customizing them for deployment scenarios with limited compute and memory is impractical. This paper introduces Flextron, a network architecture and post-training model optimization framework that supports flexible model deployment. Flextron uses a nested elastic structure that adapts rapidly to user-defined latency and accuracy targets during inference without requiring additional fine-tuning. It is also input-adaptive, automatically routing tokens through sub-networks for improved efficiency and performance. The authors propose a sample-efficient training method and routing algorithms to systematically transform an already-trained LLM into a Flextron model. Evaluation on the GPT-3 and LLaMA-2 families demonstrates Flextron’s superior performance over end-to-end trained variants and other state-of-the-art elastic networks, all with a single pretraining run that consumes only 7.63% of the tokens compared to original pretraining. 

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2. Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data

   Havrilla, Alex; Liao, Wenjing (January 2025, Advances in Neural Information Processing Systems 37 (NeurIPS 2024) Main Conference Track)

   When training deep neural networks, a model's generalization error is often observed to follow a power scaling law dependent both on the model size and the data size. Perhaps the best known example of such scaling laws are for transformer-based large language models (**LLMs**), where networks with billions of parameters are trained on trillions of tokens of text. Yet, despite sustained widespread interest, a rigorous understanding of why transformer scaling laws exist is still missing. To answer this question, we establish novel statistical estimation and mathematical approximation theories for transformers when the input data are concentrated on a low-dimensional manifold. Our theory predicts a power law between the generalization error and both the training data size and the network size for transformers, where the power depends on the intrinsic dimension d of the training data. Notably, the constructed model architecture is shallow, requiring only logarithmic depth in d. By leveraging low-dimensional data structures under a manifold hypothesis, we are able to explain transformer scaling laws in a way which respects the data geometry. Moreover, we test our theory with empirical observation by training LLMs on natural language datasets. We find the observed empirical scaling laws closely agree with our theoretical predictions. Taken together, these results rigorously show the intrinsic dimension of data to be a crucial quantity affecting transformer scaling laws in both theory and practice. 

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3. Beyond Raw Bytes: Towards Large Malware Language Models

   Kurlandski, Luke; Berger, Harel; Pan, Yin; Wright, Matthew (February 2026, NDSS 2026)

   Malware poses an increasing threat to critical computing infrastructure, driving demand for more advanced detection and analysis methods. Although raw-binary malware classifiers show promise, they are limited in their capabilities and struggle with the challenges of modeling long sequences. Meanwhile, the rise of large language models (LLMs) in natural language processing showcases the power of massive, self-supervised models trained on heterogeneous datasets, offering flexible representations for numerous downstream tasks. The success behind these models is rooted in the size and quality of their training data, the expressiveness and scalability of their neural architecture, and their ability to learn from unlabeled data in a self-supervised manner. In this work, we take the first steps toward developing large malware language models (LMLMs), the malware analog to LLMs. We tackle the core aspects of this objective, namely, questions about data, models, pretraining, and finetuning. By pretraining a malware classification model with language modeling objectives, we were able to improve downstream performance on diverse practical malware classification tasks on average by 1.1% and up to 28.6%, indicating that these models could serve to succeed raw-binary malware classifiers. 

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4. Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion

   Chen, Boyuan; Martí_Monsó, Diego; Du, Yilun; Simchowitz, Max; Tedrake, Russ; Sitzmann, Vincent (September 2024, Neural Information Processing Systems 2024)

   This paper presents Diffusion Forcing, a new training paradigm where a diffusion model is trained to denoise a set of tokens with independent per-token noise levels. We apply Diffusion Forcing to sequence generative modeling by training a causal next-token prediction model to generate one or several future tokens without fully diffusing past ones. Our approach is shown to combine the strengths of next-token prediction models, such as variable-length generation, with the strengths of full-sequence diffusion models, such as the ability to guide sampling to desirable trajectories. Our method offers a range of additional capabilities, such as (1) rolling-out sequences of continuous tokens, such as video, with lengths past the training horizon, where baselines diverge and (2) new sampling and guiding schemes that uniquely profit from Diffusion Forcing's variable-horizon and causal architecture, and which lead to marked performance gains in decision-making and planning tasks. In addition to its empirical success, our method is proven to optimize a variational lower bound on the likelihoods of all subsequences of tokens drawn from the true joint distribution. 

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5. SLOPE: DOUBLE-PRUNED SPARSE PLUS LAZY LOW-RANK ADAPTER PRETRAINING OF LLMS

   Mozaffari, M; Yazdanbakhsh, A; Zhang, Z; Dehnavi, M_Mehri (April 2025, The Thirteenth International Conference on Learning Representations)

   We propose SLOPE, a Double-Pruned Sparse Plus Lazy Low-rank Adapter Pretraining method for LLMs that improves the accuracy of sparse LLMs while accelerating their pretraining and inference and reducing their memory footprint. Sparse pretraining of LLMs reduces the accuracy of the model, to overcome this, prior work uses dense models during fine-tuning. SLOPE improves the accuracy of sparsely pretrained models by adding low-rank adapters in the final 1% iterations of pretraining without adding significant overheads to the model pretraining and inference. In addition, SLOPE uses a double-pruned backward pass formulation that prunes the transposed weight matrix using N:M sparsity structures to enable an accelerated sparse backward pass. SLOPE accelerates the training and inference of models with billions of parameters up to 1.25→ and 1.54→ respectively (OPT-33B and OPT-66B) while reducing their memory usage by up to 0.63→ and 0.61→ for training and inference respectively. 

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