How does an LLM do spacial reasoning?
A user on reddit.com is asking how Large Language Models (LLMs) perform spatial reasoning, referencing coverage of "Astra's" abilities with 3D modeling and game demos. They are curious if this capability stems from learned patterns in training data, the ability to translate natural language into 3D coordinate systems, proficiency in controlling 3D modeling programs, or a procedural generation process. The user is seeking educational resources to understand the underlying mechanisms.
- Published
- 09/07, 21:27 UTC+0
- Ingested
- 09/08, 00:00 UTC+0
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I’ve been seeing a lot of coverage of Astra’s spacial reasoning abilities, with 3d modelling demos and game demos.
Through what mechanism does an LLM “do” special training like this?
Is it through learned patterns of 3d modelling in training data?
Is it an ability to translate natural language instructions into 3d coordinate systems?
Is it really good at controlling 3d modelling programs and interfacing with them?
Is it a kind of procedural generation process, where is creating code that can do the job it’s trying to achieve?
I’d love to learn more about how this is being achieved and would appreciate any good educational links on this subject.
Edit: wanted to use the “question” flair, but it’s not available on mobile?