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As the token count increases, the model becomes more fragile.
Even small tweaks, like adjustments during fine-tuning, or the introduction of noise, can reverse earlier gains.
The point where this additional training starts to degrade performance is called the inflection point.
Once reached, the benefits of training start to become outweighed by the risk of internal instability.
The study found that this tipping point often occurs beyond 2.5 trillion tokens in smaller models, like OLMo-1B.
For AI developers chasing scale, the message seems clear: sometimes, less really is more.
