**Editorial Brief**
I taught my 1B model to follow instructions, and it got worse at doing so. This finding is noteworthy because previous research has suggested that smaller models can benefit from instruction-following techniques like Self-Training (SFT), which improves their ability to adhere to given tasks. However, this study reveals an unexpected outcome: a 1 billion parameter model actually performed worse after SFT.
The results are summarized in the table above, showing that all three models—trained from scratch at 1B, 2B, and 3B parameters—saw their Instruction-Following Evaluation (IFEval) scores decrease. The 1B model saw a drop of -5.75 points, while the 2B model decreased by -4.91 points. Interestingly, the 3B parameter model actually improved slightly, increasing its IFEval score by +2.04 points.
This finding is significant because it challenges current understandings about how instruction-following techniques affect different sized models. It raises questions about whether smaller models are indeed better candidates for SFT and if there might be a threshold below which this technique fails to improve performance. Further investigation with the 2B model at a more aggressive learning rate, as well as broader experimentation across various models and tasks, will help clarify these observations.
**Takeaways:**
– Smaller models may not benefit from instruction-following techniques like SFT.
– There is likely an optimal size for models to see improvements through such methods.
– Further research is needed to understand the mechanisms behind this phenomenon.
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