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 noisy quadratic system predicts large model test losses from N, B, K and outperforms Chinchilla's model for extrapolation up to 1000x compute.
ScheduleFree+ scales schedule-free learning to LLMs with fixes for large batches and models, outperforming Warmup-Stable-Decay schedules by up to 31% at 1000 tokens per parameter.
citing papers explorer
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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.
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Predicting Large Model Test Losses with a Noisy Quadratic System
A noisy quadratic system predicts large model test losses from N, B, K and outperforms Chinchilla's model for extrapolation up to 1000x compute.
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ScheduleFree+: Scaling Learning-Rate-Free & Schedule-Free Learning to Large Language Models
ScheduleFree+ scales schedule-free learning to LLMs with fixes for large batches and models, outperforming Warmup-Stable-Decay schedules by up to 31% at 1000 tokens per parameter.