Curriculum Learning: A Misguided Approach for LLMs?

Curriculum learning has long captivated researchers. The idea that models should train on easier tasks before tackling the hard ones seems intuitive. But does it really work? Especially large language models (LLMs) and compositional reasoning, the numbers tell a different story.

The Study

In a recent study, researchers aimed to unravel the effects of curriculum learning on LLMs. They used synthetic benchmarks in arithmetic and logic where task difficulty wasn't just about surface-level complexity. Instead, it focused on the depth of reasoning required. Surprisingly, they found no significant advantage of curriculum learning over random sampling accuracy or response length.

Let me break this down. Across different model families and training schedules, curriculum learning didn't outperform random sampling. This held true whether researchers used supervised fine-tuning or reinforcement learning methods. Essentially, the curriculum approach didn't yield the expected benefits in compositional generalization.

What This Means

For those hoping curriculum learning would unlock new levels of LLM performance, these findings are a letdown. The reality is, the specific order of training samples doesn't seem to make a meaningful difference in the context of deductive reasoning. Strip away the marketing and you might wonder if curriculum learning is overhyped.

Why should readers care? Well, curriculum learning often demands more time and computational resources. If it's not delivering on its promise, those resources might be better allocated elsewhere. The architecture matters more than the parameter count, and it seems the order of learning isn't the magic bullet some hoped it would be.

Looking Forward

Is it time to rethink our approach to training LLMs? Perhaps. While curriculum learning might have its place in other domains, its utility in improving compositional reasoning is now seriously in question. With these results in hand, researchers and engineers might need to look beyond curricular structures to improve LLM performance.

So, what's next for LLM training methods? That's still an open question, but it's clear that any future strategies will need to provide tangible, measurable benefits, something curriculum learning hasn't done in this case.