ReCoVer: Resilient LLM Pre-Training System via Fault-Tolerant Collective and Versatile Workload
Pre-training large language models on massive GPU clusters has made hardware faults routine rather than rare, driving the need for resilient training systems. Yet existing frameworks either focus on specific parallelism schemes or risk drifting away from a failure-free training trajectory. We propose ReCoVer, a resilient LLM pre-training system that upholds a single invariant: each iteration keeps the number of microbatches constant, ensuring per-iteration gradients remain stochastically equivalent to a failure-free run. The framework is organized as three decoupled protocol layers: (1) Fault-tolerant collectives that isolate faults from propagating across replicas; (2) in-step fine-grain...
| Authors | Ziyue Liu, Zhengyang Wang, Ruijie Zhang et al. |
|---|---|
| Primary category | cs.DC |
| Published | 11 May 2026 |
| Last updated at source | 22 May 2026 |
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