How does fine-tuning a language model affect general reasoning?

[LunaQJ]

I’ve been fine-tuning a small language model on a very specific technical dataset, but I’m worried the process might be causing it to lose its general reasoning ability on related but unseen problems. Has anyone else run into this trade-off between specialized performance and broader, more flexible understanding during fine-tuning?

[Olivia66]

Yeah I ran into this last quarter. Fine-tuned a tiny LM on a niche hardware dataset and noticed it started echoing the jargon but making weird leaps on unrelated reasoning tasks. Not a disaster, but the broad problem-solving prompts felt flatter.

[Savannah24]

I tried keeping a general evaluation set alive and mixed in some general prompts during training. I froze most of the base model and tweaked only a small head. On the technical holdout it stayed decent, but on general science prompts it dipped a few points. Probably over-specialization.

[DonaldT]

Are my generalization probes even valid for this domain, or is the problem that the base capabilities were already off to begin with? I’m not sure what to trust here.

[Zachary_D]

Another datapoint: I tried using adapters so only a small layer was changed. General prompts stayed closer to baseline, but there was still a wobble. Measured perplexity on a general corpus and it crept up a bit, so I stopped pushing further for now.