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> then reasoning LLMs seem to pass that bar as they do very well programming and maths competitions. it could be this is just result of good stochastic parrot

> then reasoning LLMs seem to pass that bar as they do very well programming and maths competitions. it could be this is just result of good stochastic parroting and not reasoning. Both of those niches are narrow with high amount of training data (e.g. corps buying solutions from leetcode and training LLMs on them). From another hand we see that LLMs fail in more complex environment: e.g. ask to build some new feature in postgres database.

sothatsit 1y ago
This is clearly false. LLMs being able to multiply large numbers is the clear example to me that there is more than just memorisation going on. They cannot just memorise the answers to multipling huge numbers like they do. That's not to mention that these programming competition problems are designed to be novel. They are as novel as the competition designers can get while sticking to the bounds of the competition. This is clearly not stochastic parrot behaviour. Additionally, them falling over in large codebases is not evidence that they cannot reason over smaller well-defined problems. It is just evidence that their reasoning has limits, which should not be surprising to anyone. Humans also have limits in our reasoning. That does not mean we do not reason.
- riku_iki 1y ago
  I think you just made lots of handwaving statements. Here is result which says LLMs can't do multi-digit multiplications well: https://arxiv.org/pdf/2510.00184 https://arxiv.org/pdf/2510.00184
  - sothatsit 1y ago
    We are talking about reasoning models here, not old non-reasoning models like Llama-90B and GPT-4. Obviously, they cannot multiply numbers. That was never in question. Maybe at least give a cursory glance at a paper before trying to cite it to support your point? I find it fun that this paper also points out that using another training method, IcoT, they can produce models that can multiply numbers perfectly. The frontier reasoning models can still make mistakes, they just get very close a lot of the time, even with 10-20 digit numbers. But the IcoT models can do it perfectly, they just can only multiply numbers.
    - riku_iki 1y ago
      so, give ref on results which prove that they reliably can multiply arbitrary numbers > Maybe at least give a cursory glance at a paper before trying to cite it to support your invalid point? they use CoT aka reasoning steps
      - sothatsit 1y ago
        They do not apply reinforcement learning, which is what most people mean when they talk about reasoning LLMs. This means it is not comparable to the frontier reasoning models. Here is the post I remember seeing: https://www.reddit.com/r/singularity/comments/1ip3vpa/multidigit_multiplication_performance_by_oai/ https://www.reddit.com/r/singularity/comments/1ip3vpa/multid... This shows that o3-mini has >90% accuracy at multiplying numbers up to 8-digits, and it is capable of multiplying numbers much larger than that. Whereas, gpt-4o could only multiply 2-digit numbers reliably. Here's another person I saw experimenting with this: https://sanand0.github.io/llmmath/ https://sanand0.github.io/llmmath/ That's not to mention that if you give these models a Python interpreter, they can also do this task perfectly and tackle much more complicated tasks as well. Although, that is rather separate to the models themselves being able to apply the reasoning steps to multiply numbers.
        riku_iki 1y ago
        > reinforcement learning, which is what most people mean when they talk about reasoning LLMs popularity contest has no place in tech discussion, and even then not clear on which evidence you make such statement. imo, reasoning model is model trained on lots of reasoning steps, so it is strong in producing those. rl is used in niches where there is no much training data, so data is synthetically generated, which produces lots of garbage and model need feedback to adjust. And multiplication is not such niche. > This shows that o3-mini has >90% accuracy at multiplying numbers up to 8-digits, and it is capable of multiplying numbers much larger than that. Whereas, gpt-4o could only multiply 2-digit numbers reliably. it could be just a matter that one model has training data for this and another doesn't, you can't come to any conclusion without inspecting oai data. Also, your examples actually demonstrate that frontier LLMs can't learn and reproduce trivial algorithm reliably, and results actually in a quality range of stochastic parrot.

sothatsit 1y ago

This is clearly false. LLMs being able to multiply large numbers is the clear example to me that there is more than just memorisation going on. They cannot just memorise the answers to multipling huge numbers like they do. That's not to mention that these programming competition problems are designed to be novel. They are as novel as the competition designers can get while sticking to the bounds of the competition. This is clearly not stochastic parrot behaviour. Additionally, them falling over in large codebases is not evidence that they cannot reason over smaller well-defined problems. It is just evidence that their reasoning has limits, which should not be surprising to anyone. Humans also have limits in our reasoning. That does not mean we do not reason.

riku_iki 1y ago
I think you just made lots of handwaving statements. Here is result which says LLMs can't do multi-digit multiplications well: https://arxiv.org/pdf/2510.00184 https://arxiv.org/pdf/2510.00184
- sothatsit 1y ago
  We are talking about reasoning models here, not old non-reasoning models like Llama-90B and GPT-4. Obviously, they cannot multiply numbers. That was never in question. Maybe at least give a cursory glance at a paper before trying to cite it to support your point? I find it fun that this paper also points out that using another training method, IcoT, they can produce models that can multiply numbers perfectly. The frontier reasoning models can still make mistakes, they just get very close a lot of the time, even with 10-20 digit numbers. But the IcoT models can do it perfectly, they just can only multiply numbers.
  - riku_iki 1y ago
    so, give ref on results which prove that they reliably can multiply arbitrary numbers > Maybe at least give a cursory glance at a paper before trying to cite it to support your invalid point? they use CoT aka reasoning steps
    - sothatsit 1y ago
      They do not apply reinforcement learning, which is what most people mean when they talk about reasoning LLMs. This means it is not comparable to the frontier reasoning models. Here is the post I remember seeing: https://www.reddit.com/r/singularity/comments/1ip3vpa/multidigit_multiplication_performance_by_oai/ https://www.reddit.com/r/singularity/comments/1ip3vpa/multid... This shows that o3-mini has >90% accuracy at multiplying numbers up to 8-digits, and it is capable of multiplying numbers much larger than that. Whereas, gpt-4o could only multiply 2-digit numbers reliably. Here's another person I saw experimenting with this: https://sanand0.github.io/llmmath/ https://sanand0.github.io/llmmath/ That's not to mention that if you give these models a Python interpreter, they can also do this task perfectly and tackle much more complicated tasks as well. Although, that is rather separate to the models themselves being able to apply the reasoning steps to multiply numbers.
      - riku_iki 1y ago
        > reinforcement learning, which is what most people mean when they talk about reasoning LLMs popularity contest has no place in tech discussion, and even then not clear on which evidence you make such statement. imo, reasoning model is model trained on lots of reasoning steps, so it is strong in producing those. rl is used in niches where there is no much training data, so data is synthetically generated, which produces lots of garbage and model need feedback to adjust. And multiplication is not such niche. > This shows that o3-mini has >90% accuracy at multiplying numbers up to 8-digits, and it is capable of multiplying numbers much larger than that. Whereas, gpt-4o could only multiply 2-digit numbers reliably. it could be just a matter that one model has training data for this and another doesn't, you can't come to any conclusion without inspecting oai data. Also, your examples actually demonstrate that frontier LLMs can't learn and reproduce trivial algorithm reliably, and results actually in a quality range of stochastic parrot.