Deep Dive into Train-Infer Mismatches: Causes from Floating-Point to Architecture
nrehiew_ · x · 2026-08-19
This technical article explores the various causes of mismatched results between model training and inference. Beyond the well-known floating-point reduction errors, it covers architecture-specific issues, such as details within GDN (Generalized Divisive Normalization) and sparse attention mechanisms.
It serves as a valuable reference for engineers who need to ensure consistent model behavior across different deployment stages.
Related event: Fixing Train-Infer Mismatch Boosts Open-Source RL Performance(2 posts)→
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