From Good Text to Gibberish in Two Steps: Uncovering LLM Finetuning Instability
ivan_bezdomny · x · 2026-08-02
The author points out that while finetuning APIs may seem accessible to everyday users, it is crucial to understand how these models fail.
- Training Fragility: In just two training steps, a model can degrade from writing high-quality articles to producing complete gibberish.
- Persistent Issues: Models remain highly sensitive to basic issues like training data duplication and repeated phrases, echoing instabilities struggled with seven years ago.
- Quantization Worsens Stability: Quantization further degrades robustness, often requiring grid searches for learning rates to find the maximum stable value.
Related event: Developer Reveals LLM Fine-Tuning Fragility and Training Crashes(3 posts)→
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