Unverified50% confidenceSolutionExact time
In the post-training phase, Motif adopts a "failure-driven SFT" strategy: identifying model weaknesses through benchmark testing and internal scenarios, then generating targeted datasets for training.
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10/4/2026
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VerifiedLarge model training goes through three core stages: pre-training, supervised fine-tuning (SFT), and preference alignment.75% similarVerifiedDuring SFT training, the model only computes loss on the assistant portion (loss masking technique), meaning it only learns how to answer, not how to ask questions.75% similarUnverified诊断训练瓶颈的核心效率差异来自于是否提前建立了性能基线（baseline），如已知模型健康状态下的MFU则可快速发现偏离73% similarUnverifiedCompared to full pre-training, SFT requires far less data and compute, but still requires a GPU cluster, a distributed training framework, and checkpoint management and experiment tracking capabilities.73% similarUnverifiedThere is a scarce supply of talent capable of diagnosing systematic model failures, designing training data strategies, and performing inference efficiency engineering optimizations (such as model quantization and KV Cache design).72% similar
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