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LinkedIn's new approach accelerates AI model training by optimizing the serving of large teacher models within a distillation pipeline, addressing a critical bottleneck for scaling recommendation systems. The strategy combines online inference with offline precomputation and memory-efficient frameworks to handle high-volume query loads without sacrificing iteration speed. This highlights significant progress in the practical engineering required to deploy compact, LLM-supervised rankers at enterprise scale.
How LinkedIn Trains AI Job Search 8x Faster with Multi-Teacher Distillation
LinkedIn has published details of the training infrastructure behind its AI-powered job search, describing a multi-teacher distillation pipeline that compresses knowledge from large teacher models into a compact 0.6B-parameter ranking model. By Claudio Masolo
LinkedIn has published details of the training infrastructure behind its AI-powered job search, describing a multi-teacher distillation pipeline that compresses knowledge from large teacher models into a compact 0.6B-parameter ranking model. The main contribution isn’t just the distillation technique. It’s the systems work that makes it fast enough for iteration. This includes a custom framework built on SGLang, which serves teacher models directly in the training loop. Training a small language model (SLM)…
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