Nathan explores whether classic linear programming frameworks can be repurposed to navigate neural network weight spaces directly—offering a fresh angle on interpolation, merging, and understanding model behavior without relying on fine-tuning or gradient descent.
Background
Model merging and weight-space interpolation are increasingly popular techniques for combining large language models without full fine-tuning, but they lack strong theoretical grounding.
- Source
- Hacker News (RSS)
- Published
- Nov 11, 2026 at 08:30 PM
- Score
- 7.0 / 10