Does fine-tuning solve the "lost in the middle" problem in 2026?

All answers to this question.

The "lost in the middle" issue is largely an architectural flaw in how models were trained. However, specialized fine-tuning on "Long-Context Alignment" datasets has shown massive improvements this year. By training the model to attend to the entire sequence equally, you can effectively expand its reliable reasoning range. We did this for a legal tech app where the model had to cross-reference clauses 50 pages apart. While prompting alone failed 40% of the time, the fine-tuned version handled it with 95% precision. It’s about teaching the model that the "middle" is just as important as the beginning and end.

   Answered 2025-01-07 by Victoria Hayes

Victoria, did you find that this fine-tuning affected the model's general performance on shorter, simpler tasks?

   Answered 2025-01-09 by Samuel Porter

• Samuel, we saw a slight "catastrophic forgetting" on creative writing tasks, but for technical reasoning, it actually improved. We keep a generalist model for chat and the tuned one for document analysis.

     Commented 2025-01-10 by Douglas Grant

I still prefer RAG for this. Fine-tuning for long context is great, but retrieval allows you to scale to millions of documents, not just a few hundred pages.

   Answered 2025-01-11 by Brian Taylor

• I agree with Brian. Unless you absolutely need to reason across the entire document at once, retrieval is more scalable and less prone to training noise.

     Commented 2025-01-12 by Diane Murphy