7 ms·

I like Raschka's writing, even if he is considerably more optimistic about this tech than I am. But I think it's inappropriate to claim that models like R1 are

I like Raschka's writing, even if he is considerably more optimistic about this tech than I am. But I think it's inappropriate to claim that models like R1 are "good at deductive or inductive reasoning" when that is demonstrably not true, they are incapable of even the simplest "out-of-distribution" deductive reasoning: https://xcancel.com/JJitsev/status/1883158738661691878 https://xcancel.com/JJitsev/status/1883158738661691878 They are certainly capable of doing is a wide variety of computations that simulate reasoning, and maybe that's good enough for your use case. But it is unpredictably brittle unless you spend a lot on o1-pro (and even then...). Raschka has a line about "whether and how an LLM actually 'thinks' is a separate discussion" but this isn't about semantics. R1 clearly sucks at deductive reasoning and you will not understand "reasoning" LLMs if you take DeepSeek's claims at face value. It seems especially incurious for him to copy-paste the "a-ha moment" from Deepseek's technical report without critically investigating it. DeepSeek's claims are unscientific, without real evidence, and seem focused on hype and investment: This moment is not only an "aha moment" for the model but also for the researchers observing its behavior. It underscores the power and beauty of reinforcement learning: rather than explicitly teaching the model on how to solve a problem, we simply provide it with the right incentives, and it autonomously develops advanced problem-solving strategies. The "aha moment" serves as a powerful reminder of the potential of RL to unlock new levels of intelligence in artificial systems, paving the way for more autonomous and adaptive models in the future. Perhaps it was able to solve that tricky Olympiad problem, but there are an infinite variety of 1st grade math problems it is not able to solve. I doubt it's even reliably able to solve simple variations of that root problem. Maybe it is! But it's frustrating how little skepticism there is about CoT, reasoning traces, etc.

scarmig 2y ago
> But I think it's inappropriate to claim that models like R1 are "good at deductive or inductive reasoning" when that is demonstrably not true, they are incapable of even the simplest "out-of-distribution" deductive reasoning: https://xcancel.com/JJitsev/status/1883158738661691878 https://xcancel.com/JJitsev/status/1883158738661691878 Your link says that R1, not all models like R1, fails at generalization. Of particular note: > We expose DeepSeek R1 to the variations of AIW Friends problem and compare model behavior to o1-preview, o1-mini and Claude 3.5 Sonnet. o1-preview handles the problem robustly, DeepSeek R1 shows strong fluctuations across variations with distribution very similar to o1-mini.
- Legend2440 2y ago
  The way the authors talk about LLMs really rubs me the wrong way. They spend more of the paper talking up the 'claims' about LLMs that they are going to debunk than actually doing any interesting study. They came into this with the assumption that LLMs are just a cheap trick. As a result, they deliberately searched for an example of failure, rather than trying to do an honest assessment of generalization capabilities.
  - suddenlybananas 2y ago
    >They came into this with the assumption that LLMs are just a cheap trick. As a result, they deliberately searched for an example of failure, rather than trying to do an honest assessment of generalization capabilities. And lo and behold, they still found a glaring failure. You can't fault them for not buying into the hype.
    - Legend2440 2y ago
      But it is still dishonest to declare reasoning LLMs a scam simply because you searched for a failure mode. If given a few hundred tries, I bet I could find an example where you reason poorly too. Wikipedia has a whole list of common failure modes of human reasoning: https://en.wikipedia.org/wiki/List_of_fallacies https://en.wikipedia.org/wiki/List_of_fallacies
      - daveguy 2y ago
        Well, given the success rate is no more than 90% in the best cases. You could probably find a failure in about 10 tries. The only exception is o1-preview. And this is just a simple substitution of parameters.
  - o11c 2y ago
    What the hype crowd doesn't get is that for most people, "a tool that randomly breaks" is not useful.
Legend2440 2y ago
>But I think it's inappropriate to claim that models like R1 are "good at deductive or inductive reasoning" when that is demonstrably not true, they are incapable of even the simplest "out-of-distribution" deductive reasoning: That's not actually what your link says. The tweet says that it solves the simple problem (that they originally designed to foil base LLMs) so they had to invent harder problems until they found one it could not reliably solve.
- suddenlybananas 2y ago
  Did you see how similar the more complicated problem is? It's nearly the exact same problem.
blovescoffee 2y ago
The other day I fed a complicated engineering doc for an architectural proposal at work into R1. I incorporated a few great suggestions into my work. Then my work got reviewed very positively by a large team of senior/staff+ engineers (most with experience at FAANG; ie credibly solid engineers). R1 was really useful! Sorry you don’t like it but I think it’s unfair to say it sucks at reasoning.
- martin-t 2y ago
  [flagged]
  - blovescoffee 2y ago
    [flagged]
  - DiogenesKynikos 2y ago
    How do I know you're reasoning, and not just simulating reasoning (imperfectly)?
  - dang 2y ago
    Please don't cross into personal attack and please don't post in the flamewar style, regardless of how wrong someone is or you feel they are. We're trying for the opposite here. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
    - martin-t 2y ago
      The issue with this approach to moderation is that it targets posts based on visibility of "undesired" behavior instead of severity. For example, many manipulative tactics (e.g. the fake sorry here, responding to something else than was said, ...) and lying can be considered insults (they literally assume the reader is not smart enough to notice, hence at least as severe as calling someone an idiot) but it's hard for a mod to notice without putting in a lot of effort to understand the situation. Yet when people (very mildly) punish this behavior by calling it out, they are often noticed by the mod because the call out is more visible.
_yb2s 2y ago
> they are incapable of even the simplest "out-of-distribution" deductive reasoning But the link demonstrates the opposite- these models absolutely are able to reason out of distribution, just not with perfect fidelity. The fact that they can do better than random is itself really impressive. And o1-preview does impressively well, only vary rarely getting the wrong answer on variants of that Alice in Wonderland problem. If you would listen to most of the people critical of LLMs saying they're a "stochastic parrot" - it should be impossible for them to do better than random on any out of distribution problem. Even just changing one number to create a novel math problem should totally stump them and result in entirely random outputs, but it does not. Overall, poor reasoning that is better than random but frequently gives the wrong answer is fundamentally, categorically entirely different from being incapable of reasoning.
- danielmarkbruce 2y ago
  anyone saying an LLM is a stochastic parrot doesn't understand them... they are just parroting what they heard.
  - bloomingkales 2y ago
    There is definitely a mini cult of people that want to be very right about how everyone else is very wrong about AI.
    - danielmarkbruce 2y ago
      ie, the people that AI is dumb? Or you are saying I'm in a cult for being pro it - I'm definitely part of that cult - the "we already have agi and you have to contort yourself into a pretzel to believe otherwise" cult. Not sure if there is a leader though.
      - bloomingkales 2y ago
        I didn't realize my post can be interpreted either way. I'll leave it ambiguous, hah. Place your bets I guess.
energy123 2y ago
This is basically a misrepresentation of that tweet.
k__ 2y ago
"researchers seek to leverage their human knowledge of the domain, but the only thing that matters in the long run is the leveraging of computation" - Rich Sutton

scarmig 2y ago

> But I think it's inappropriate to claim that models like R1 are "good at deductive or inductive reasoning" when that is demonstrably not true, they are incapable of even the simplest "out-of-distribution" deductive reasoning: https://xcancel.com/JJitsev/status/1883158738661691878 https://xcancel.com/JJitsev/status/1883158738661691878 Your link says that R1, not all models like R1, fails at generalization. Of particular note: > We expose DeepSeek R1 to the variations of AIW Friends problem and compare model behavior to o1-preview, o1-mini and Claude 3.5 Sonnet. o1-preview handles the problem robustly, DeepSeek R1 shows strong fluctuations across variations with distribution very similar to o1-mini.

Legend2440 2y ago
The way the authors talk about LLMs really rubs me the wrong way. They spend more of the paper talking up the 'claims' about LLMs that they are going to debunk than actually doing any interesting study. They came into this with the assumption that LLMs are just a cheap trick. As a result, they deliberately searched for an example of failure, rather than trying to do an honest assessment of generalization capabilities.
- suddenlybananas 2y ago
  >They came into this with the assumption that LLMs are just a cheap trick. As a result, they deliberately searched for an example of failure, rather than trying to do an honest assessment of generalization capabilities. And lo and behold, they still found a glaring failure. You can't fault them for not buying into the hype.
  - Legend2440 2y ago
    But it is still dishonest to declare reasoning LLMs a scam simply because you searched for a failure mode. If given a few hundred tries, I bet I could find an example where you reason poorly too. Wikipedia has a whole list of common failure modes of human reasoning: https://en.wikipedia.org/wiki/List_of_fallacies https://en.wikipedia.org/wiki/List_of_fallacies
    - daveguy 2y ago
      Well, given the success rate is no more than 90% in the best cases. You could probably find a failure in about 10 tries. The only exception is o1-preview. And this is just a simple substitution of parameters.
- o11c 2y ago
  What the hype crowd doesn't get is that for most people, "a tool that randomly breaks" is not useful.

Legend2440 2y ago

>But I think it's inappropriate to claim that models like R1 are "good at deductive or inductive reasoning" when that is demonstrably not true, they are incapable of even the simplest "out-of-distribution" deductive reasoning: That's not actually what your link says. The tweet says that it solves the simple problem (that they originally designed to foil base LLMs) so they had to invent harder problems until they found one it could not reliably solve.

suddenlybananas 2y ago
Did you see how similar the more complicated problem is? It's nearly the exact same problem.

blovescoffee 2y ago

The other day I fed a complicated engineering doc for an architectural proposal at work into R1. I incorporated a few great suggestions into my work. Then my work got reviewed very positively by a large team of senior/staff+ engineers (most with experience at FAANG; ie credibly solid engineers). R1 was really useful! Sorry you don’t like it but I think it’s unfair to say it sucks at reasoning.

martin-t 2y ago
[flagged]
- blovescoffee 2y ago
  [flagged]
- DiogenesKynikos 2y ago
  How do I know you're reasoning, and not just simulating reasoning (imperfectly)?
- dang 2y ago
  Please don't cross into personal attack and please don't post in the flamewar style, regardless of how wrong someone is or you feel they are. We're trying for the opposite here. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
  - martin-t 2y ago
    The issue with this approach to moderation is that it targets posts based on visibility of "undesired" behavior instead of severity. For example, many manipulative tactics (e.g. the fake sorry here, responding to something else than was said, ...) and lying can be considered insults (they literally assume the reader is not smart enough to notice, hence at least as severe as calling someone an idiot) but it's hard for a mod to notice without putting in a lot of effort to understand the situation. Yet when people (very mildly) punish this behavior by calling it out, they are often noticed by the mod because the call out is more visible.

_yb2s 2y ago

> they are incapable of even the simplest "out-of-distribution" deductive reasoning But the link demonstrates the opposite- these models absolutely are able to reason out of distribution, just not with perfect fidelity. The fact that they can do better than random is itself really impressive. And o1-preview does impressively well, only vary rarely getting the wrong answer on variants of that Alice in Wonderland problem. If you would listen to most of the people critical of LLMs saying they're a "stochastic parrot" - it should be impossible for them to do better than random on any out of distribution problem. Even just changing one number to create a novel math problem should totally stump them and result in entirely random outputs, but it does not. Overall, poor reasoning that is better than random but frequently gives the wrong answer is fundamentally, categorically entirely different from being incapable of reasoning.

danielmarkbruce 2y ago
anyone saying an LLM is a stochastic parrot doesn't understand them... they are just parroting what they heard.
- bloomingkales 2y ago
  There is definitely a mini cult of people that want to be very right about how everyone else is very wrong about AI.
  - danielmarkbruce 2y ago
    ie, the people that AI is dumb? Or you are saying I'm in a cult for being pro it - I'm definitely part of that cult - the "we already have agi and you have to contort yourself into a pretzel to believe otherwise" cult. Not sure if there is a leader though.
    - bloomingkales 2y ago
      I didn't realize my post can be interpreted either way. I'll leave it ambiguous, hah. Place your bets I guess.

energy123 2y ago

This is basically a misrepresentation of that tweet.

k__ 2y ago

"researchers seek to leverage their human knowledge of the domain, but the only thing that matters in the long run is the leveraging of computation" - Rich Sutton