It’s also quite likely that something like Auto-GPT would work a lot better using a version of LLM that had been fine-tuned/reinforcement-trained for this specific usecase—just as Chat-GPT is a lot more effective as a chatbot than the underlying GPT-3 model was before the specialized training. If the LLM is optimized for the wrapper and the wrapper designed to make efficient use of the entire context-size of the LLM, thinks are going to work a lot better.
7 months later, we now know that this is true. Also, we now know that you can take output from a prompted/scaffolded LLM and use it to fine-tune another LLM to do the same things without needing prompt/scaffold.
It’s also quite likely that something like Auto-GPT would work a lot better using a version of LLM that had been fine-tuned/reinforcement-trained for this specific usecase—just as Chat-GPT is a lot more effective as a chatbot than the underlying GPT-3 model was before the specialized training. If the LLM is optimized for the wrapper and the wrapper designed to make efficient use of the entire context-size of the LLM, thinks are going to work a lot better.
7 months later, we now know that this is true. Also, we now know that you can take output from a prompted/scaffolded LLM and use it to fine-tune another LLM to do the same things without needing prompt/scaffold.
Could you please point out the work you have in mind here?