Delta Force: How Weak Models are Secretly the Best Teachers
In the world of LLM fine-tuning, stronger usually means better. But what if we’ve been looking at supervision all wrong? A provocative new paper introduces the Delta Learning Hypothesis, arguing that LLMs can learn just as well—sometimes even better—from weak data, as long as it’s paired. The trick isn’t in the absolute quality of the training signals, but in the difference—the delta—between them. Like a coach pointing out small improvements, even bad examples can teach if they highlight how one is slightly better than another. ...