There’s a new Apple analysis paper making the rounds, and for those who’ve seen the reactions, you’d assume it simply toppled all the LLM business. That’s removed from true, though it may be the very best try and deliver to the mainstream a dialogue that the ML group has been having for ages. Right here is why this paper issues.
The paper in query, The Phantasm of Pondering: Understanding the Strengths and Limitations of Reasoning Fashions through the Lens of Drawback Complexity, is actually fascinating. It systematically probes so-called Massive Reasoning Fashions (LRMs) like Claude 3.7 and DeepSeek-R1 utilizing managed puzzles (Tower of Hanoi, Blocks World, and so on.), as an alternative of ordinary math benchmarks that always endure from knowledge contamination.
The outcomes? LRMs do higher than their LLM cousins at medium complexity duties, however collapse simply as laborious on extra advanced ones. And worse, as duties get tougher, these “reasoning” fashions begin pondering much less, no more, even after they nonetheless have token finances left to spare.
However whereas this paper is making headlines as if it simply uncovered some deep secret, I’d argue: none of that is new. It’s simply clearer now, and simpler for the broader public to know. That, in actual fact, is nice information.
What the paper reveals
The headline takeaway is that fashions marketed for “reasoning” nonetheless fail on issues a affected person little one can grasp. Within the Tower of Hanoi, for instance, fashions like Claude and o3-mini collapse after seven or eight disks. And even when given the precise answer algorithm and requested to easily comply with it, efficiency doesn’t enhance.
In different phrases, they aren’t reasoning, however somewhat iteratively extending LLM inference patterns in additional elaborate methods. That distinction issues, and it’s the true worth of the Apple paper. The authors are pushing again on loaded phrases like “reasoning” and “pondering,” which counsel symbolic inference and planning when what’s truly occurring is only a layered sample extension: the mannequin runs a number of inference passes till it lands on one thing that sounds believable.
This isn’t precisely a revelation. Meta’s AI Chief Yann LeCun has lengthy in contrast right now’s LLMs to “home cats” and has been vocal that AGI gained’t come from Transformers. Subbarao Kambhampati has printed for years about how “chains of thought” don’t correspond to how these fashions truly compute. And Gary Marcus, properly, his long-held “deep studying is hitting a wall” thesis will get one other feather in its cap.
Sample matching, not drawback fixing
The research’s most damning knowledge level may be this: when complexity goes up, fashions actually cease attempting. They scale back their very own inner “pondering” as challenges scale, regardless of having loads of compute finances left. This isn’t only a technical failure, however somewhat a conceptual one.
What Apple’s paper helps make clear is that many LLMs aren’t failing as a result of they “haven’t educated sufficient” or “simply want extra knowledge.” They’re failing as a result of they essentially lack a method to symbolize and execute step-by-step algorithmic logic. And that’s not one thing chain-of-thought prompting or reinforcement fine-tuning can brute-force away.
To cite the paper itself: “LRMs fail to make use of specific algorithms and purpose inconsistently throughout puzzles.” Even when handed an answer blueprint, they stumble.
So… Is This Dangerous Information?
Sure. Simply not new information.
These outcomes don’t come as a giant shock to anybody deeply embedded within the ML analysis group. However the buzz they’ve generated highlights one thing extra fascinating: the woder public may lastly be able to grapple with distinctions the ML world has been making for years, significantly round what fashions like these can and can’t do.
This distinction is necessary. When folks name these methods “pondering,” we begin treating them as if they’ll substitute issues they’re presently incapable of doing. That’s when the hallucinations and logic failures go from fascinating quirks to harmful blind spots.
That is why Apple’s contribution issues. Not as a result of it “uncovered” LLMs, however as a result of it helps draw clearer traces round what they’re and what they’re not. And that readability is lengthy overdue.
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