It’s cool that this works (at least a bit)! It reminds me of the world models in RL agents. As these have an encoder, decoder, and latent space predictor (conditional on action). I wonder how long it will be before someone uses LLM’s an explicit world model in an agent.
Given the general power of pretrained LLM’s, it may help with the data efficiency of RL agents (ignoring the LLM pretraining).
Making an agent won’t help with alignment, but having a world model (and its associated state) to inspect might.
It’s cool that this works (at least a bit)! It reminds me of the world models in RL agents. As these have an encoder, decoder, and latent space predictor (conditional on action). I wonder how long it will be before someone uses LLM’s an explicit world model in an agent.
Given the general power of pretrained LLM’s, it may help with the data efficiency of RL agents (ignoring the LLM pretraining).
Making an agent won’t help with alignment, but having a world model (and its associated state) to inspect might.