Exploring Fine-Tuning for In-Context Retrieval and Efficient KV-Caching in Long-Context Language Models

• URL: http://arxiv.org/abs/2601.18527v1
• Date: Mon, 26 Jan 2026 14:37:02 GMT
• Title: Exploring Fine-Tuning for In-Context Retrieval and Efficient KV-Caching in Long-Context Language Models
• Authors: Francesco Maria Molfese, Momchil Hardalov, Rexhina Blloshmi, Bill Byrne, Adrià de Gispert,
• Abstract summary: Long-Context Language Models can encode entire document collections.<n>Experiments show substantial in-domain improvements, achieving gains of up to +20 points over the base model.<n>We show that our fine-tuning approaches bring moderate improvements in robustness under KV-cache compression.
• Score: 25.173231793620417
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: With context windows of millions of tokens, Long-Context Language Models (LCLMs) can encode entire document collections, offering a strong alternative to conventional retrieval-augmented generation (RAG). However, it remains unclear whether fine-tuning strategies can improve long-context performance and translate to greater robustness under KV-cache compression techniques. In this work, we investigate which training strategies most effectively enhance LCLMs' ability to identify and use relevant information, as well as enhancing their robustness under KV-cache compression. Our experiments show substantial in-domain improvements, achieving gains of up to +20 points over the base model. However, out-of-domain generalization remains task dependent with large variance -- LCLMs excels on finance questions (+9 points), while RAG shows stronger performance on multiple-choice questions (+6 points) over the baseline models. Finally, we show that our fine-tuning approaches bring moderate improvements in robustness under KV-cache compression, with gains varying across tasks.

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