Notes on DeepSeek-V4's training system

Fireworks Training is in preview, offering a platform to train and deploy frontier models, with a focus on DeepSeek-V4's innovative training system that emphasizes a programmable loop over fixed recipes. DeepSeek-V4 integrates architecture, routing, reward modeling, reasoning modes, and agent execution into the training process, necessitating a flexible infrastructure that supports distributed execution, inference integration, and scaling. The system explores various strategies, such as hybrid attention with memory hierarchy, anticipatory routing to address stability issues, and different reasoning modes like Non-think, Think High, and Think Max, each with distinct training recipes. Additionally, DeepSeek-V4 employs a generative reward model for tasks challenging to evaluate with scalar rewards, and uses On-Policy Distillation to merge domain specialists into a single model without directly merging weights. The platform also supports agentic training, preserving reasoning traces across interactions and incorporating Quick Instruction tokens for auxiliary decisions. The overarching theme is the shift towards a programmable training infrastructure capable of adapting to runtime, evaluation, and system integration needs, as embodied by the Fireworks Training API, which aims to handle the complexities of modern training systems.

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