Overcoming Prior Barriers: Supervised Fine-Tuning under Long-Tail Distribution
Quick summary
arXiv:2610.12345v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) adapts pretrained large language models (LLMs) to downstream tasks, but the required concepts can receive substantially different levels of pretrained support. Frequent concepts are more likely to be well learned, whereas rare concepts may remain weakly represented. We introduce a novel notion named prior barrier to quantify how strongly the pretrained model supports competing concepts over the target concept. We observe that prior barriers follow a long-tail distribution, placing head and tail concepts at different s
Key takeaways
- arXiv:2610.12345v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) adapts pretrained large language models (LLMs) to downstream tasks, but the required concepts can receive substantially different levels of pretrained support.
- Frequent concepts are more likely to be well learned, whereas rare concepts may remain weakly represented.
- We introduce a novel notion named prior barrier to quantify how strongly the pretrained model supports competing concepts over the target concept.
Why it matters
“Overcoming Prior Barriers: Supervised Fine-Tuning under Long-Tail Distribution” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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