Scaling LLM Post-Training at Netflix Baolin Li, Lingyi Liu, Binh Tang, Shaojing Li Introduction Pre-training gives Large Language Models (LLMs) broad linguistic ability and general world knowledge …

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Netflix built an internal post-training framework to scale LLM fine-tuning from experimentation to production. The framework abstracts infrastructure complexity across four dimensions: data (streaming, sequence packing, loss masking), model (sharding, LoRA, architecture support), compute (distributed job orchestration, checkpointing, MFU monitoring), and workflow (supporting both SFT and on-policy RL). Key engineering decisions include staying Hugging Face-compatible for interoperability, maintaining optimized internal model implementations for performance, and evolving from SPMD-only execution to hybrid orchestration for RL workflows. The system enables researchers to focus on modeling rather than distributed systems plumbing.

Scaling LLM Post-Training at Netflix

A Model Developer’s Post-Training Journey

Learnings from Building the Post-Training Framework

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