Why AI's "Reasoning" Fails When Problems Get Hard
Psych 🧠 - 424/500
Reader,
Apple recently published a very intereseting paper that caught my attention.
Titled “The illusion of thinking”, the paper dives deep into Large Reasoning Models (LRMs), looking beyond just whether they get the right answer, and exploring how they think and where they stumble.
What is it?
The study systematically examines the reasoning processes of Large Reasoning Models (LRMs) using controllable puzzle environments.
This allows precise manipulation of complexity and inspection of internal reasoning traces.
The authors aim to identify limitations in LRM capabilities and provide insights for future advancements.
Key Findings:
Frontier LRMs failed to develop generalizable problem solving for problems beyond specific complexity.
There is a limit in a LRM's reasoning effort based off of its problem (in the same way you can only do so much bench pressing before you drop.)
LRMs are not good at exact computation and can be inconsistent across puzzle types.
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What do I need to know:
LRMs have strengths in some aspects of reasoning but are not quite as advanced as they seem.
The amount of thought has limits as models get confused and reach a point where they are less effecitive due to complexity, in comparison to regular language.
This research contributes to a deeper understanding of AI reasoning and its limitations.
Source:
https://machinelearning.apple.com/research/illusion-of-thinking