Characterization and Mitigation of Training Instabilities in Microscaling Formats

• URL: http://arxiv.org/abs/2506.20752v1
• Date: Wed, 25 Jun 2025 18:25:08 GMT
• Title: Characterization and Mitigation of Training Instabilities in Microscaling Formats
• Authors: Huangyuan Su, Mujin Kwun, Stephanie Gil, Sham Kakade, Nikhil Anand,
• Abstract summary: Training large language models is an expensive, compute-bound process.<n>Next-generation hardware accelerators increasingly support lower-precision arithmetic formats.<n>We investigate the challenges and viability of block-scaled precision formats during model training.
• Score: 6.025438902954768
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training large language models is an expensive, compute-bound process that must be repeated as models scale, algorithms improve, and new data is collected. To address this, next-generation hardware accelerators increasingly support lower-precision arithmetic formats, such as the Microscaling (MX) formats introduced in NVIDIA's Blackwell architecture. These formats use a shared scale within blocks of parameters to extend representable range and perform forward/backward GEMM operations in reduced precision for efficiency gains. In this work, we investigate the challenges and viability of block-scaled precision formats during model training. Across nearly one thousand language models trained from scratch -- spanning compute budgets from $2 \times 10^{17}$ to $4.8 \times 10^{19}$ FLOPs and sweeping over a broad range of weight-activation precision combinations -- we consistently observe that training in MX formats exhibits sharp, stochastic instabilities in the loss, particularly at larger compute scales. To explain this phenomenon, we conduct controlled experiments and ablations on a smaller proxy model that exhibits similar behavior as the language model, sweeping across architectural settings, hyperparameters, and precision formats. These experiments motivate a simple model in which multiplicative gradient bias introduced by the quantization of layer-norm affine parameters and a small fraction of activations can trigger runaway divergence. Through \emph{in situ} intervention experiments on our proxy model, we demonstrate that instabilities can be averted or delayed by modifying precision schemes mid-training. Guided by these findings, we evaluate stabilization strategies in the LLM setting and show that certain hybrid configurations recover performance competitive with full-precision training. We release our code at https://github.com/Hither1/systems-scaling.

Related papers

• Recipes for Pre-training LLMs with MXFP8 [0.0]
  Precision scaling has emerged as a compelling technique for improving GPU efficiency without sacrificing accuracy.<n>MX-formats offer improved numeric stability compared to other reduced-precision representations.<n>We show an improved rounding mode, which uses round-to-infinity to compute scaling factors, enables successful pre-training in MXFP8 for an 8B model on 15T tokens.
  arXiv  Detail & Related papers  (2025-05-30T21:08:15Z)
• Scaling Laws for Upcycling Mixture-of-Experts Language Models [17.796361238003403]
  Pretraining large language models (LLMs) is resource-intensive, often requiring months of training time even with high-end GPU clusters.<n>There are two approaches of mitigating such computational demands: reusing smaller models to train larger ones (upcycling) and training computationally efficient models like mixture-of-experts (MoE)
  arXiv  Detail & Related papers  (2025-02-05T09:11:13Z)
• Latent Thought Models with Variational Bayes Inference-Time Computation [52.63299874322121]
  Latent Thought Models (LTMs) incorporate explicit latent thought vectors that follow an explicit prior model in latent space.<n>LTMs demonstrate superior sample and parameter efficiency compared to autoregressive models and discrete diffusion models.
  arXiv  Detail & Related papers  (2025-02-03T17:50:34Z)
• Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference [55.150117654242706]
  We show that model selection for computation-aware GPs trained on 1.8 million data points can be done within a few hours on a single GPU. As a result of this work, Gaussian processes can be trained on large-scale datasets without significantly compromising their ability to quantify uncertainty.
  arXiv  Detail & Related papers  (2024-11-01T21:11:48Z)
• Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models [56.00251589760559]
  Large language models (LLMs) can act as gradient priors in a zero-shot setting.<n>We introduce LM-GC, a novel method that integrates LLMs with arithmetic coding.<n>Experiments indicate that LM-GC surpasses existing state-of-the-art lossless compression methods.
  arXiv  Detail & Related papers  (2024-09-26T13:38:33Z)
• Pruning Large Language Models with Semi-Structural Adaptive Sparse Training [17.381160429641316]
  Adaptive Sparse Trainer (AST) is a novel and efficient retraining framework tailored for semi-structured sparse models.<n>AST reduces the perplexity and zero-shot accuracy gap between dense and 2:4 semi-structured sparse models to 0.6 and 1.16%, respectively.
  arXiv  Detail & Related papers  (2024-07-30T06:33:44Z)
• The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute [66.84421705029624]
  We introduce an experimental protocol that enables model comparisons based on equivalent compute, measured in accelerator hours. We pre-process an existing large, diverse, and high-quality dataset of books that surpasses existing academic benchmarks in quality, diversity, and document length. This work also provides two baseline models: a feed-forward model derived from the GPT-2 architecture and a recurrent model in the form of a novel LSTM with ten-fold throughput.
  arXiv  Detail & Related papers  (2023-09-20T10:31:17Z)
• GSB: Group Superposition Binarization for Vision Transformer with Limited Training Samples [46.025105938192624]
  Vision Transformer (ViT) has performed remarkably in various computer vision tasks. ViT usually suffers from serious overfitting problems with a relatively limited number of training samples. We propose a novel model binarization technique, called Group Superposition Binarization (GSB)
  arXiv  Detail & Related papers  (2023-05-13T14:48:09Z)
• MoEfication: Conditional Computation of Transformer Models for Efficient Inference [66.56994436947441]
  Transformer-based pre-trained language models can achieve superior performance on most NLP tasks due to large parameter capacity, but also lead to huge computation cost. We explore to accelerate large-model inference by conditional computation based on the sparse activation phenomenon. We propose to transform a large model into its mixture-of-experts (MoE) version with equal model size, namely MoEfication.
  arXiv  Detail & Related papers  (2021-10-05T02:14:38Z)
• Extrapolation for Large-batch Training in Deep Learning [72.61259487233214]
  We show that a host of variations can be covered in a unified framework that we propose. We prove the convergence of this novel scheme and rigorously evaluate its empirical performance on ResNet, LSTM, and Transformer.
  arXiv  Detail & Related papers  (2020-06-10T08:22:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.