Action LLMs work by generating text underneath. Just some higher level software interprets the text generated and do some action. So the immediate inference result is still text.
Obviously there is a lot of variety in architecture, but in the prototypical example there are vision and languages encoders and an action decoder which decodes direction into action steps. Eg, Hugging Face SmolVLA:
> Specifically, the VLM processes sensorimotor states, including images from multiple RGB cameras, and a language instruction describing the task. In turn, the VLM outputs features directly fed to the action expert, which outputs the final 3 continuous actions.[1]
Or NVidia's GR00T N1:
> A diffusion transformer (DiT) processes the robot’s proprioceptive state and action, which are then cross-attended with image and text tokens from the Eagle-2 VLM backbone to output the denoised motor actions.[2]
Depends entirely on VLA arch. Some have dedicated action diffusion heads that work in a standalone non-text action output space. Much like an LLM can either use an external TTS or have audio output heads attached to it directly for native S2S.
But your entire premise is wrong regardless of that.
Even if VLAs were forever bound to outputting text, you'd have to prove that they're fundamentally incapable of emitting text that maps to useful action sequences. No proof of that whatsoever - and plenty of empirical evidence suggests otherwise. Even non-specialist LLMs like ChatGPT are getting better at controlling robots and navigating 3D environments, if slowly.
>But your entire premise is wrong regardless of that.
You don't understand what I am saying. The crux of your misunderstanding is here
>emitting text that maps to useful action sequences
If you have a static mapping from text to action, then you are throwing away all the advantage of using an AI. The whole point of AI is that you can get an output from an input without explicit mapping. So If you use explicit mapping anywhere in the chain, then you lose most of the advantage of using the AI.
So if your hardware, physical vocabulary is limited, like move left/right/up/down then what you say could work. But something that have the dexterity of a human form, this vocabulary is nearly infinite. You won't be able to use explicit mapping there.
You can literally have an LLM output target joint angles. As text. To be decoded by an explicit decoder, and executed by the robot. Some early VLAs did exactly that.
Your entire premise is wrong.
Modern action decoders are different, and usually take the form of neural networks trained end to end jointly with the rest of the model. Not fundamentally more expressive, just more in line with what we want.
I'm repeating "what you claim to be impossible was done 3 years ago and was already replaced with better versions of the same idea and you are hilariously out of touch".
As I suspected you are fooled by this video and imagine it to be capable of much more than what is shown. This video is pretty non-marketing and is quite straight to the point. But that does not prevent you from being awed!
So What is LLM is used here for? It is used for mere translation between different robots. So it is mostly symbolic translation.
What I am talking about is to translation LLM inference directly to movements. For example, if you ask an LLM, how do I open the microwave door? It will list the steps. I am talking about a system that can go from "put the thing in the microwave", to action steps, without having to never once demonstrate it physically, and do it just from LLM inference.
In short, the way LLMs used here is not (categorically) the way I was asking about.
That is a straight forward task. I am talking about using an LLM to come up with a sequence of complex steps that does not have an intermediate textural representation.
So it should be something like, "put back this slipped cycle chain back on sprocket"..
The thing literally has a diffusion "action expert" sit in the same attention system as a pre-trained VLM. And the VLM itself is ALSO trained to generate raw actions as a part of the training recipe (the first paper) - it just doesn't do it at inference time. What the "action expert" does is parallelize the action generation process - based on VLM's internal states.
It's exactly the thing you claimed to be impossible. Described in detail in a paper from 2025. What's your excuse?
I have not overlooked anything. I had imagined that this "mapping", to have any power, would also need to be handled by an LLM (action expert). But here is the problem with that. That would not be as "intelligent" as an LLM....And you can't make it as smart as the LLM because there is not a similarly huge training data on which LLMs are trained on..
The backbone of the VLA there is literally a pre-trained Gemma model. And a small one at that.
You already downgraded your claims from "LLMs are irrelevant to robotics" to a measly "you can't train a useful robotics LLM because there's not enough data". And you say that while looking at an LLM that was pre-trained on all of internet scraped and only then reused for robotics.
Both the pool of robotics-relevant data and the performance of foundation model LLMs grow over time. All the companies that are serious about robotics are serious about scaling up data collection.
I'm not going to claim that this "LLM core" approach is the best approach to AI robotics possible - but if you're betting on it failing outright, you're going to be fighting uphill.
That does not help a lot.