arXiv:2607.07023v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) is often treated as a capability-adaptation step, while alignment is attributed to later preference optimization or reinforcement learning. This separation is incomplete: when examples are scored and kept online during fine-tuning, the choice of which data to train on already changes the model's behavioral preferences. We study online data selection as an implicit alignment mechanism. Given the same base model, optimizer, and selected-token budget, we compare random, loss-based, quality-based, and diversity-based onli
The increasing sophistication of AI models and the ongoing challenges in controlling their behavior make research into implicit alignment mechanisms critical.
Understanding how data selection impacts model behavior can lead to more efficient and controllable AI development, reducing reliance on costly and complex post-training alignment methods.
The conventional separation between capability adaptation and alignment is challenged, suggesting that data curation itself is a potent alignment tool.
- · AI developers
- · Data scientists
- · AI safety researchers
- · Inefficient AI alignment methods
- · Organizations with poor data curation strategies
Improved efficiency and effectiveness of AI fine-tuning processes.
Reduced incidence of unintended model behaviors and biases in deployed AI systems.
Accelerated development of more reliable and ethical AI, impacting wider adoption and trust.
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Read at arXiv cs.LG