Introduces the first benchmark for fine-grained failures in reinforcement fine-tuning of LLMs and an automatic management framework that detects, diagnoses, and remediates them.
TRANSOM: An efficient fault-tolerant system for training LLMs
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TierCheck is a cluster-aware tiered checkpointing system that uses local memory for fast differential recovery and remote persistent storage for base checkpoints to reduce overhead and enable high-frequency checkpointing in LLM training.
Production-scale empirical study of a 63-node 504-GPU cluster reports multi-signal failure detection needs, low checkpoint bandwidth utilization, heavy-tailed node exclusions, and 2.7x higher success for auto-retry chains.
citing papers explorer
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Towards Robust LLM Post-Training: Automatic Failure Management for Reinforcement Fine-Tuning
Introduces the first benchmark for fine-grained failures in reinforcement fine-tuning of LLMs and an automatic management framework that detects, diagnoses, and remediates them.
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TierCheck: Tiered Checkpointing for Fault Tolerance in Large Language Model Training
TierCheck is a cluster-aware tiered checkpointing system that uses local memory for fast differential recovery and remote persistent storage for base checkpoints to reduce overhead and enable high-frequency checkpointing in LLM training.
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From Detection to Recovery: Operational Analysis on LLM Pre-training with 504 GPUs
Production-scale empirical study of a 63-node 504-GPU cluster reports multi-signal failure detection needs, low checkpoint bandwidth utilization, heavy-tailed node exclusions, and 2.7x higher success for auto-retry chains.