[HN Gopher] Theoretical limitations of multi-layer Transformer
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Theoretical limitations of multi-layer Transformer
Author : fovc
Score : 62 points
Date : 2025-01-31 17:48 UTC (5 hours ago)
(HTM) web link (arxiv.org)
(TXT) w3m dump (arxiv.org)
| cs702 wrote:
| Huh. I just skimmed this and quickly concluded that it's
| definitely _not_ light reading.
|
| It sure looks and smells like good work, so I've added it to my
| reading list.
|
| Nowadays I feel like my reading list is growing faster than I can
| go through it.
| Matthyze wrote:
| I'd humbly like to ask people who've read the paper whether
| it's worth trying to understand it without a great math
| background. The paper looks intersting but daunting, and I'd
| hate to sink a lot of time into it and leave defeated.
|
| It sometimes sucks being in ML with 'only' a CS background.
| Feels like all the math and physics grads are running around
| having fun with their fancy mathematics, while I stand here,
| feeling dimwitted.
| aketchum wrote:
| Most of it is linear algebra and convex optimization. You can
| learn a lot of it with free resources from MIT, Stanford,
| Georgia Tech, or YouTube. If you want more of a school style
| learning environment you can enroll in the Georgia Tech OMSCS
| program and just take the classes related to the math etc
| that you are interested in. No reason you have to graduate
| and it is maybe $800 a course.
| Matthyze wrote:
| Thanks! Now might actually be a great time for me to pick
| up the material. If anyone has suggestions about
| particularly good free/online mathematics courses for ML,
| I'd really love to hear it. Or books!
| szundi wrote:
| Would be fun to ask o3
| svachalek wrote:
| I'm not a math person either, but I'm familiar with some of
| the terminology. The two things I got out of this paper were:
|
| 1. Depth is more important than breadth for making
| transformers smarter. That is, for a given size of model it
| will be more powerful with more, smaller layers than it would
| be with less, bigger ones. Interestingly, Mistral just
| updated their small model yesterday with a big reduction in
| layers in order to improve performance. Among the many ways
| they say it more technically they do say directly "depth
| plays a more critical role than width in reasoning and
| composition tasks".
|
| 2. As I understand it, they are claiming to be able to prove
| mathematically that Chain of Thought such as seen in the new
| DeepSeek R1 and GPT o1/o3 models creates results that
| wouldn't be possible without it. The thought chains
| effectively act as additional layers, and per the previous
| point, the more layers the better. "From a theoretical view,
| CoT provides Transformer with extra computation space, and
| previous work ... proved that log-precision Transformer with
| CoT could simulate any polynomial-time algorithm. Therefore,
| by further assuming certain complexity conjecture ... their
| results imply that constant depth Transformer with CoT could
| simulate poly-time algorithm, while constant depth Transform
| ... itself can not solve P-complete task."
| nighthawk454 wrote:
| A nice trick for most papers is to skip the middle (at
| first). Just don't read most of the lines of math. Focus on
| the inputs and outputs.
|
| If the premise and conclusion don't make sense on
| fundamentals the math isn't likely to fix it. Most lines are
| literally equals signs - just walking you through some
| equivalencies as proof. A large statement saying "If ABC,
| then ... (and then ... and then ... and then ...) and finally
| XYZ"
|
| The middle 'and then's aren't really that important if the
| conclusion XYZ isn't interesting. Or much more commonly, the
| ABC premise is false anyway so who cares.
|
| Most readers I'd wager are not sitting here deciphering
| opaque gradient derivations every single paper. Just skip it
| unless it proves worthy
| cubefox wrote:
| Loosely related thought: A year ago, there was a lot of talk
| about the Mamba SSM architecture replacing transformers.
| Apparently that didn't happen so far.
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