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/1883158738661691878They 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.
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:
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.