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You’re absolutely wrong! This is not how reasoning models work. Chain-of-thought did not produce reasoning models.

by svnt 1y ago

lucb1e 1y ago
Then I can't explain why it's producing the results that it does. If you have more information to share, I'm happy to update my knowledge... Doing a web search on the topic just comes up with marketing materials. Even Wikipedia's "Reasoning language model" article is mostly a list of release dates and model names, with as only relevant-sounding remark as to how these models are different: "[LLMs] can be fine-tuned on a dataset of reasoning tasks paired with example solutions and step-by-step (reasoning) traces. The fine-tuned model can then produce its own reasoning traces for new problems." It sounds like just another dataset: more examples, more training, in particular on worked examples where this "think step by step" method is being demonstrated with known-good steps and values. I don't see how that fundamentally changes how it works; you're saying such models do not predict the most likely token for a given context anymore, that there is some fundamentally different reasoning process going on somewhere?
- svnt 1y ago
  I'm saying adding "think step by step" does not get you close to actual reasoning, it just produces marginally self-consistent linguistic reasoning. Actual reasoning requires training on diverse data sources, as you noted, but also coached experimentation (supervised fine-tuning) not just adding "think step by step" instruction to a model trained on typical textual datasets. "Think step by step" came first and produced increased performance on a variety of tasks, but was overhyped in its approximation of reasoning.
Dylan16807 1y ago
How do they work then? Because I thought chain of thought made for reasoning. And the first google result for 'chain of thought versus reasoning models' says it does: https://medium.com/@mayadakhatib/the-era-of-reasoning-models-moving-beyond-chain-of-thought-prompting-fa24ce25c106 https://medium.com/@mayadakhatib/the-era-of-reasoning-models... Give me a better source.
- svnt 1y ago
  Did you even read the article you posted? It supports my statement. CoT produces the linguistic scaffolding for reasoning, but doesn't actually provide much accuracy in doing so. e.g. https://developer.nvidia.com/blog/maximize-robotics-performance-by-post-training-nvidia-cosmos-reason/ https://developer.nvidia.com/blog/maximize-robotics-performa...
  - Dylan16807 1y ago
    The article I posted says that chain of thought is enough to make something a reasoning model. It's listed as one of the types. It says that reasoning models "now aim for" more, but that doesn't disqualify the older more basic techniques. I'm not sure what I'm supposed to learn from your link because there's so much vision stuff happening here and that's the focus of the article. Can you link me something about what you need to make a bare minimum reasoning LLM? Text only.

You’re absolutely wrong! This is not how reasoning models work. Chain-of-thought did not produce reasoning models.

by svnt 1y ago

You’re absolutely wrong! This is not how reasoning models work. Chain-of-thought did not produce reasoning models.

Then I can't explain why it's producing the results that it does. If you have more information to share, I'm happy to update my knowledge... Doing a web search on the topic just comes up with marketing materials. Even Wikipedia's "Reasoning language model" article is mostly a list of release dates and model names, with as only relevant-sounding remark as to how these models are different: "[LLMs] can be fine-tuned on a dataset of reasoning tasks paired with example solutions and step-by-step (reasoning) traces. The fine-tuned model can then produce its own reasoning traces for new problems." It sounds like just another dataset: more examples, more training, in particular on worked examples where this "think step by step" method is being demonstrated with known-good steps and values. I don't see how that fundamentally changes how it works; you're saying such models do not predict the most likely token for a given context anymore, that there is some fundamentally different reasoning process going on somewhere?

svnt 1y ago
I'm saying adding "think step by step" does not get you close to actual reasoning, it just produces marginally self-consistent linguistic reasoning. Actual reasoning requires training on diverse data sources, as you noted, but also coached experimentation (supervised fine-tuning) not just adding "think step by step" instruction to a model trained on typical textual datasets. "Think step by step" came first and produced increased performance on a variety of tasks, but was overhyped in its approximation of reasoning.

Dylan16807 1y ago

How do they work then? Because I thought chain of thought made for reasoning. And the first google result for 'chain of thought versus reasoning models' says it does: https://medium.com/@mayadakhatib/the-era-of-reasoning-models-moving-beyond-chain-of-thought-prompting-fa24ce25c106 https://medium.com/@mayadakhatib/the-era-of-reasoning-models... Give me a better source.

svnt 1y ago
Did you even read the article you posted? It supports my statement. CoT produces the linguistic scaffolding for reasoning, but doesn't actually provide much accuracy in doing so. e.g. https://developer.nvidia.com/blog/maximize-robotics-performance-by-post-training-nvidia-cosmos-reason/ https://developer.nvidia.com/blog/maximize-robotics-performa...
- Dylan16807 1y ago
  The article I posted says that chain of thought is enough to make something a reasoning model. It's listed as one of the types. It says that reasoning models "now aim for" more, but that doesn't disqualify the older more basic techniques. I'm not sure what I'm supposed to learn from your link because there's so much vision stuff happening here and that's the focus of the article. Can you link me something about what you need to make a bare minimum reasoning LLM? Text only.