Supervised fine-tuning (SFT) of large language models (LLMs) is a critical step for adapting models to downstream tasks, but it is computationally expensive and can suffer from overfitting or bias amplification when using the full dataset. Existing online batch selection methods that dynamically score and filter samples during training have limitations. They often rely solely on data utility, neglecting diversity, depend on external resources like reference models or validation sets, and incur extra training time over full-dataset training.
According to a new paper on arXiv by authors Heming Zou, Yixiu Mao, Yun Qu, Qi Wang, and Xiangyang Ji, a framework called UDS (Utility-Diversity Sampling) addresses these challenges. UDS leverages the nuclear norm of the logits matrix to capture both data utility and intra-sample diversity, while estimating inter-sample diversity through efficient low-dimensional embedding comparisons with a lightweight memory buffer of historical samples. This design eliminates the need for external resources and unnecessary backpropagation, securing computational efficiency.
"UDS consistently outperforms state-of-the-art online batch selection methods under varying data budgets, and significantly reduces training time compared to full-dataset fine-tuning." — from the paper's abstract.
Performance Gains
Experiments on multiple benchmarks demonstrate UDS's advantages. The framework achieves better model performance across different data budgets while requiring less computation than full-dataset SFT. Key benefits include:
- Reduced training time: Compared to full-dataset fine-tuning, UDS significantly cuts training duration.
- Improved model quality: Outperforms existing online batch selection methods consistently.
- No external resources: Unlike prior work, UDS does not need a reference model or validation set.
- Built-in diversity: Considers both inter- and intra-sample diversity, preventing overfitting and bias.
Comparison of Batch Selection Approaches
| Feature | Existing Methods | UDS |
|---|---|---|
| Data utility used | Yes | Yes |
| Diversity considered | Often neglected | Both intra- and inter-sample |
| External resources required | Reference model or validation set | None |
| Extra training time over full dataset | Yes | No |
| Performance vs. SOTA | Variable | Consistently outperforms |
Implications for Enterprise AI
For CTOs and technology leaders investing in LLM deployment, the UDS framework offers a practical route to reduce the cost and time of supervised fine-tuning. By automating the selection of the most valuable training examples, enterprises can achieve high-performance domain-adapted models without the expense of full-dataset processing. The code is available at the URL provided in the paper, enabling teams to integrate UDS into their existing SFT pipelines. This efficiency gain is critical as organizations scale their AI capabilities across applications such as supply chain optimization, trade document processing, and logistics automation, where quickly fine-tuning LLMs on proprietary data can yield competitive advantages.
The elimination of external dependencies also simplifies infrastructure requirements, aligning with lean IT strategies. As LLM adoption accelerates in enterprise contexts, frameworks like UDS that balance utility and diversity while minimizing computational overhead will become increasingly valuable.
