arXiv:2605.26924v1 Announce Type: new Abstract: Large language models (LLMs) have achieved remarkable progress, with post-training playing a crucial role in enhancing their reasoning capabilities. Among post-training paradigms, supervised fine-tuning (SFT) is widely used: it leverages external data to provide dense supervision and enables efficient training. However, directly fine-tuning on expert data can hurt generalization when the data distribution is mismatched with the target model's own distribution. In this work, we propose Data Adaptation for Reasoning Tuning (DART), which formulates
The rapid advancement of LLMs necessitates more efficient and adaptable fine-tuning methods as models are deployed across diverse applications and data environments.
Improving the generalization capabilities of LLMs through data adaptation tackles a key limitation in their practical deployment, making them more robust and versatile.
This research suggests a more effective way to fine-tune LLMs, potentially leading to models that perform better across a wider range of tasks even with mismatched training data.
- · AI developers
- · Companies deploying custom LLMs
- · Users of LLM-powered applications
- · AI development methods reliant on perfectly matched datasets
LLMs become more adaptable and performant in real-world scenarios with varied data distributions.
Reduced need for extensive, domain-specific data collection for fine-tuning, accelerating LLM deployment cycles.
Broader adoption of AI agents and specialized AI applications due to more robust reasoning capabilities across diverse tasks.
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Read at arXiv cs.CL