Why is validation loss plateauing and outputs repeating in LM fine-tuning?

[Ethan.W]

I’ve been trying to fine-tune a small language model on a very specific technical dataset, but I’m hitting a wall where the validation loss just plateaus and then the model starts outputting repetitive nonsense. I’m worried my training data might have some hidden redundancy or noise that’s causing this model collapse. Has anyone else run into this during their own projects?

[JamesB]

Yeah, I’ve seen that. The validation loss stalled and the model started looping on the same tokens. In my case there were near duplicates and some templated passages in the dataset. I removed duplicates, added a bit more varied material from related sources, and shuffled more aggressively. After that the plateau softened a bit, but it didn’t disappear overnight.

[Ava_J]

Another time I suspected data leakage. I found a few same prompts leaked into training and validation, so the model learned to memorize those patterns rather than generalize. After I fixed the split so val was truly unseen, performance steadied and the repeats dropped. It was a relief but also a reminder that the split can ruin your picture fast.

[Joshua_P]

I tried tweaking training dynamics too. Lower learning rate, a touch of gradient clipping, and a tiny dropout in the later layers. The curve looked a little smoother for a couple of epochs, then back to the same loop. I’m not sure if the issue is data quality or the model size for that niche.

[Mark_J]

Do you think the hidden redundancy is the real culprit, or could the problem be that the model is just too small for the domain and the repeats are a symptom of memorization?