Scaling Laws Revisited for Reasoning Tasks
How the classic scaling-law relationship between compute, data, and performance behaves differently for multi-step reasoning versus raw language modeling.
The original scaling laws
Early scaling-law research showed that language modeling loss improves predictably as a power law of compute, data, and parameter count — a finding that guided years of 'just make it bigger' strategy.
What's different for reasoning
Multi-step reasoning benchmarks show a less smooth relationship — performance can plateau on harder problems even as raw language-modeling loss keeps improving, suggesting reasoning ability doesn't scale as a simple byproduct of scale alone.
Why it matters
This is part of why the field has shifted toward explicit reasoning-time compute (longer chains of thought, self-verification) rather than relying purely on larger pretraining runs to improve reasoning.