Is fine-tuning having a moment again? After being overshadowed by bigger, shinier models, it’s creeping back into the conversation—and this time, it might actually stick. (Kevin Kuipers, Sota)
Summary
- Fine-tuning stages a comeback, as new tools, approaches, and demand for control spark a shift from generic to bespoke AI.
- Modularity, serverless workflows, and custom training algorithms redefine the modern fine-tuning stack, blending comfort and granularity.
- Evaluation challenges persist, but online reinforcement learning could help fine-tuning into continuous learning.
