ReCoVer maintains constant microbatch counts per iteration via fault-tolerant collectives, in-step recovery, and versatile workload redistribution to preserve training trajectory on up to 512 GPUs despite losing 256, yielding 2.23× higher effective throughput than checkpoint-restart.
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A neural network learns parameter-dependent viscosity models for ice that satisfy physical invariants and generalize from velocity or stress data.
WestWorld introduces a scalable trajectory world model with Sys-MoE routing via system embeddings and structural embeddings for physical knowledge, pretrained on 89 environments to improve zero-shot prediction and real-robot control.
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ReCoVer: Resilient LLM Pre-Training System via Fault-Tolerant Collective and Versatile Workload
ReCoVer maintains constant microbatch counts per iteration via fault-tolerant collectives, in-step recovery, and versatile workload redistribution to preserve training trajectory on up to 512 GPUs despite losing 256, yielding 2.23× higher effective throughput than checkpoint-restart.