But that AI engineer who is implementing speculative decoding is still just doing basic plumbing that has little to do with the actual reasoning. Yes, he/she might make the process faster, but they will know just as little about why/how the reasoning works as when they implemented a naive, slow version of the inference.

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roadside_picnic 6 hours ago | parent [-]

What "actual reasoning" are you referring to? I believe you're making my point for me.

Speculative decoding requires the implementer to understand:

- How the initial prompt is processed by the LLM

- How to retrieve all the probabilities of previously observed tokens in the prompt (this also help people understand things like the probability of the entire prompt itself, the entropy of the prompt etc).

- Details of how the logits generate the distribution of next tokens

- Precise details of the sampling process + the rejection sampling logic for comparing the two models

- How each step of the LLM is run under-the-hood as the response is processed.

Hardly just plumbing, especially since, to my knowledge, there are not a lot of hand-holding tutorials on this topic. You need to really internalize what's going on and how this is going to lead to a 2-5x speed up in inference.

Building all of this yourself gives you a lot of visibility into how the model behaves and how "reasoning" emerges from the sampling process.

edit: Anyone who can perform speculative decoding work also has the ability to inspect the reasoning steps of an LLM and do experiments such as rewinding the thought process of the LLM and substituting a reasoning step to see how it impacts the results. If you're just prompt hacking you're not going to be able to perform these types of experiments to understand exactly how the model is reasoning and what's important to it.

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amelius 5 hours ago | parent [-]

But I can make a similar argument about a simple multiplication:

- You have to know how the inputs are processed.

- You have to left-shift one of the operands by 0, 1, ... N-1 times.

- Add those together, depending on the bits in the other operand.

- Use an addition tree to make the whole process faster.

Does not mean that knowing the above process gives you a good insight in the concept of A*B and all the related math and certainly will not make you better at calculus.

	▲	roadside_picnic 5 hours ago \| parent [-]
		I'm still confused by what you meant by "actual reasoning", which you didn't answer. I also fail to understand how building what you described would not help your understanding of multiplication, I think it would mean you understand multiplication much better than most people. I would also say that if you want to be a "multiplication engineer" then, yes you should absolutely know how to do what you've described there. I also suspect you might have lost the main point. The original comment I was replying to stated: > Don't really see why you'd need to understand how the transformer works to do LLMs at work. I'm not saying implementing speculative decoding is enough to "fully understand LLMs". I'm saying if you can't at least implement that, you don't understand enough about LLMs to really get the most out of them. No amount of twiddling around with prompts is going to give you adequate insight into how an LLMs works to be able to build good AI tools/solutions.