Training LLMs is Like Riding a Motorcycle: Fragility to Data Duplication
ivan_bezdomny · x · 2026-08-02
Detailing a gradient explosion case during GRPO training, the author points out the persistent fragility of current large models. Even after years of development, models remain highly unstable when facing basic issues like training data duplication and the recurrence of specific rare phrases, a problem worsened by quantization. The author emphasizes the necessity of grid-searching for the highest stable learning rate, concluding that training LLMs requires the dynamic balance of riding a motorcycle, not the crude simplicity of driving a tractor.
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