arXiv:2606.19542v1 Announce Type: new Abstract: Large language models are commonly aligned through supervised fine-tuning, yet little is known about how their internal representations evolve during this process. We study alignment dynamics using persistent homology by tracking the topology of activation spaces throughout fine-tuning. Across four transformer language models ranging from 1B to 7B parameters and three alignment objectives corresponding to helpful, harmless, and mixed training data, we find that the majority of topological reorganization occurs during the earliest stages of traini
This research provides deeper insight into the internal workings and optimization processes of large language models, a critical area given their rapid adoption and integration across industries.
Understanding how LLMs learn and adapt through fine-tuning is crucial for developing more robust, controllable, and efficient AI systems, impacting their safety, reliability, and further deployment.
The ability to track and analyze the 'topology of activation spaces' offers a new methodological lens for AI researchers to diagnose and improve model alignment, potentially leading to more targeted fine-tuning strategies.
- · AI researchers
- · LLM developers
- · AI safety organizations
Improved understanding of LLM fine-tuning dynamics.
Development of more stable, interpretable, and ethically aligned AI models.
Accelerated progress in general-purpose AI due to better foundational understanding of learning mechanisms.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG