Language models struggle with simplicity: Study reveals LLMs fail at Occam's Razor reasoning

A new research paper examines a critical gap in how we evaluate large language models: whether they prefer simpler explanations over more complex ones, a principle known as Occam's Razor. While LLMs have shown promise in various reasoning tasks, a team of researchers set out to understand whether they actually follow the core principle that simpler explanations are better when multiple valid answers exist.

The reasoning problem#

Inductive and abductive reasoning are essential for solving real-world problems where many hypotheses could fit the available evidence. In such situations, Occam's Razor — the philosophical principle favoring the simplest viable explanation — typically provides the most useful guidance. Yet most existing evaluations of LLM reasoning abilities ignore whether models actually produce simpler, more parsimonious responses.

How the researchers tested this#

The team developed a framework to synthetically generate reasoning tasks that require both inductive and abductive reasoning simultaneously, expressible in first-order logic. They then created an automated metric to measure whether hypotheses generated by LLMs actually conform to Occam's Razor — with high-quality answers being both correct and the simplest possible.

What they found#

Testing state-of-the-art language models revealed significant limitations. While LLMs can perform inductive and abductive reasoning on straightforward scenarios, they struggle markedly when dealing with more intricate world models. More critically, even when reasoning-enhancing techniques like in-context learning and reinforcement learning from verification (RLVR) were applied, the models continued to produce overly complex hypotheses rather than simpler, more elegant solutions.

Why it matters#

This finding suggests a fundamental gap in current LLM capabilities: the ability to prioritize simplicity alongside correctness. As these models are increasingly deployed for scientific discovery, problem-solving, and decision-making, their tendency to generate unnecessarily complex explanations could lead to suboptimal or inefficient conclusions in real-world applications.