We need a consensus on how to call these architectures. LMCA sounds fine to me. All in all, a very nice writeup. I did my own brief overview of alignment problems of such agents here. I would love to collaborate and do some discussion/research together. What’s your take on how these LCMAs may self-improve and how to possibly control it?
Interesting. I gave a strong upvote to that post, and I looked at your longer previous one a bit too. It looks like you’d seen this coming farther out than I had. I expected LLMs to be agentized somehow, but I hadn’t seen how easy the episodic memory and tool use was.
There are a number of routes for self-improvement, as you lay out, and ultimately those are going to be the real medium-term concern if these things work well. I haven’t thought about LMCAs self-improvement as much as human improvement; this post is a call for the alignment community to think about this at all. Oh well, time will tell shortly if this approach gets anywhere, and people will think about it when it happens. I was hoping we’d get out ahead of it.
Thanks. My concern is that I don’t see much effort in alignment community to work on this thing, unless I’m missing something. Maybe you know of such efforts? Or was that perceived lack of effort the reason for this article? I don’t know how much I can keep up this independent work, and I would love if there was some joint effort to tackle this. Maybe an existing lab, or an open-source project?
Calling attention to this approach and getting more people to at least think about working on it is indeed the purpose of this post. I also wanted to stress-test the claims to see if anyone sees reasons that LMCAs won’t build on and improve LLM performance, and thereby be the default stand for inclusion in deployment. I don’t know of anyone actually working on this as of yet.
We need a consensus on how to call these architectures. LMCA sounds fine to me.
All in all, a very nice writeup. I did my own brief overview of alignment problems of such agents here.
I would love to collaborate and do some discussion/research together.
What’s your take on how these LCMAs may self-improve and how to possibly control it?
Interesting. I gave a strong upvote to that post, and I looked at your longer previous one a bit too. It looks like you’d seen this coming farther out than I had. I expected LLMs to be agentized somehow, but I hadn’t seen how easy the episodic memory and tool use was.
There are a number of routes for self-improvement, as you lay out, and ultimately those are going to be the real medium-term concern if these things work well. I haven’t thought about LMCAs self-improvement as much as human improvement; this post is a call for the alignment community to think about this at all. Oh well, time will tell shortly if this approach gets anywhere, and people will think about it when it happens. I was hoping we’d get out ahead of it.
Thanks.
My concern is that I don’t see much effort in alignment community to work on this thing, unless I’m missing something. Maybe you know of such efforts? Or was that perceived lack of effort the reason for this article?
I don’t know how much I can keep up this independent work, and I would love if there was some joint effort to tackle this. Maybe an existing lab, or an open-source project?
Calling attention to this approach and getting more people to at least think about working on it is indeed the purpose of this post. I also wanted to stress-test the claims to see if anyone sees reasons that LMCAs won’t build on and improve LLM performance, and thereby be the default stand for inclusion in deployment. I don’t know of anyone actually working on this as of yet.
I hadn’t seen your post. Reading it now.