# Robust LLM Training Infrastructure at ByteDance

arXiv: https://arxiv.org/abs/2509.16293 · HTML: https://mlsanity.com/paper/2509.16293/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 84 references that are in the map.
- First posted on arXiv 19 Sep 2025.

## Builds on (most cited first, 30 of 42)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding](https://mlsanity.com/paper/2006.16668/) (2020)
- [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://mlsanity.com/paper/1910.02054/) (2019)
- [Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://mlsanity.com/paper/2204.07705/) (2022)
- [Training Deep Nets with Sublinear Memory Cost](https://mlsanity.com/paper/1604.06174/) (2016)
- [PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel](https://mlsanity.com/paper/2304.11277/) (2023)
- [Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM](https://mlsanity.com/paper/2104.04473/) (2021)
- [Ring Attention with Blockwise Transformers for Near-Infinite Context](https://mlsanity.com/paper/2310.01889/) (2023)
- [Seed1.5-VL Technical Report](https://mlsanity.com/paper/2505.07062/) (2025)
- [ZeRO-Offload: Democratizing Billion-Scale Model Training](https://mlsanity.com/paper/2101.06840/) (2021)
- [Reducing Activation Recomputation in Large Transformer Models](https://mlsanity.com/paper/2205.05198/) (2022)
- [DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models](https://mlsanity.com/paper/2309.14509/) (2023)
- [MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs](https://mlsanity.com/paper/2402.15627/) (2024)
- [Large Language Models and Games: A Survey and Roadmap](https://mlsanity.com/paper/2402.18659/) (2024)
- [Pathways: Asynchronous Distributed Dataflow for ML](https://mlsanity.com/paper/2203.12533/) (2022)
- [Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads](https://mlsanity.com/paper/1901.05758/) (2019)
- [FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel Fusion](https://mlsanity.com/paper/2406.06858/) (2024)
- [Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large DNNs](https://mlsanity.com/paper/2204.12013/) (2022)
- [Characterization of Large Language Model Development in the Datacenter](https://mlsanity.com/paper/2403.07648/) (2024)

## Built on by (most cited first, 16 of 16)

- [Laminar: A Scalable Asynchronous RL Post-Training Framework](https://mlsanity.com/paper/2510.12633/) (2025)
- [AgentNoiseBench: Benchmarking Robustness of Tool-Using LLM Agents Under Noisy Condition](https://mlsanity.com/paper/2602.11348/) (2026)
- [RLBoost: Harvesting Preemptible Resources for Cost-Efficient Reinforcement Learning on LLMs](https://mlsanity.com/paper/2510.19225/) (2025)
- [Training LLMs with Fault Tolerant HSDP on 100,000 GPUs](https://mlsanity.com/paper/2602.00277/) (2026)
- [xLLM Technical Report](https://mlsanity.com/paper/2510.14686/) (2025)
- [Exploring Silent Data Corruption as a Reliability Challenge in LLM Training](https://mlsanity.com/paper/2604.00726/) (2026)
- [GhostServe: A Lightweight Checkpointing System in the Shadow for Fault-Tolerant LLM Serving](https://mlsanity.com/paper/2605.00831/) (2026)
- [Role-Based Fault Tolerance System for LLM RL Post-Training](https://mlsanity.com/paper/2512.22492/) (2025)
- [DataStates-LLM: Scalable Checkpointing for Transformer Models Using Composable State Providers](https://mlsanity.com/paper/2601.16956/) (2026)
- [ReCoVer: Resilient LLM Pre-Training System via Fault-Tolerant Collective and Versatile Workload](https://mlsanity.com/paper/2605.11215/) (2026)
- [A Few GPUs, A Whole Lotta Scale: Faithful LLM Training Emulation with PrismLLM](https://mlsanity.com/paper/2605.15617/) (2026)
- [SCOUT: Symmetric Consensus Outlier Detection for Failure Localization in LLM Pre-Training](https://mlsanity.com/paper/2608.11034/) (2026)
- [TrainSDC: Characterizing and Mitigating Silent Data Corruption in Large Language Model Training](https://mlsanity.com/paper/2608.30769/) (2026)
- [Leto: Fast In-Place Recovery for LLM Training on Surviving Hardware](https://mlsanity.com/paper/2610.00687/) (2026)
- [Zephon: Elastic Determinism for Online, Stateful Foundation Model Data Loading Pipelines](https://mlsanity.com/paper/2610.03087/) (2026)
- [TSRL: Trigger-based Supervised Reinforcement Learning for Continual Agent Training](https://mlsanity.com/paper/2610.17726/) (2026)

## Abstract (arXiv)

The training scale of large language models (LLMs) has reached tens of thousands of GPUs and is still continuously expanding, enabling faster learning of larger models. Accompanying the expansion of the resource scale is the prevalence of failures (CUDA error, NaN values, job hang, etc.), which poses significant challenges to training stability. Any large-scale LLM training infrastructure should strive for minimal training interruption, efficient fault diagnosis, and effective failure tolerance to enable highly efficient continuous training. This paper presents ByteRobust, a large-scale GPU infrastructure management system tailored for robust and stable training of LLMs. It exploits the uniqueness of LLM training process and gives top priorities to detecting and recovering failures in a routine manner. Leveraging parallelisms and characteristics of LLM training, ByteRobust enables high-capacity fault tolerance, prompt fault demarcation, and localization with an effective data-driven approach, comprehensively ensuring continuous and efficient training of LLM tasks. ByteRobust is deployed on a production GPU platform and achieves 97% ETTR for a three-month training job on 9,600 GPUs.