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Supervised fine-tuning (SFT) has been the dominant technique for years to adapt pre-trained language models to specific tasks. However, this process often involves a hidden cost: the degradation of previously learned capabilities. By forcing the model to select only the observed label, the rich preference structure that the model had developed during its pre-training is neglected, especially in contexts where multiple plausible alternatives exist. Recent studies on Shannon and Renyi entropy reveal that base models exhibit a regular multimodal structure, where entropy peaks indicate points with several valid options. This information is key to preserving the diversity and quality of the model's internal knowledge.
To address this challenge, LP-SFT (Local-Preserving Supervised Fine-Tuning) emerges, a fine-tuning objective that explicitly protects that inherent entropy structure. At each step, LP-SFT builds an adaptive support of alternative tokens and applies a locally normalized preservation loss, maintaining the relative structure of the base model while optimizing the supervised token with cross-entropy. Experiments show that this technique improves the balance between accuracy (pass@1) and diversity (pass@k), outperforming both classic SFT and other recent improvements. This is especially relevant for enterprise applications where a robust model is needed that does not lose exploration capability or generate artificially limited responses.
In this context, having a technology partner that understands these complexities makes the difference. At Q2BSTUDIO we develop artificial intelligence solutions for businesses that integrate advanced fine-tuning and knowledge preservation techniques. Our team implements AI agents, custom models, and analysis systems based on multimodal architectures, always prioritizing quality and adaptability. Additionally, we offer custom applications and custom software that allow organizations to deploy these technologies in production environments, whether on-premise or through AWS and Azure cloud services. The combination of local preservation with scalable infrastructure ensures that your model maintains both accuracy and diversity, without compromising performance or data cybersecurity.
For companies looking to maximize the value of their data, we also provide business intelligence services with Power BI and automation processes that are powered by optimized language models. The ability to maintain a rich entropy structure during fine-tuning translates into more reliable reports, more natural virtual assistants, and recommendation systems that truly understand domain uncertainty. Q2BSTUDIO combines technical expertise with a practical approach, helping its clients extract the full potential of AI without losing sight of the statistical fundamentals that make it work.
