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[NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models
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I don't know far "pure" next-token prediction can go with planning, although I wouldn't count them out until their performance starts noticeably plateauing. But the tree of thought architecture is a very similar concept to what you're discussing, you should definitely give it a read: https://arxiv.org/abs/2305.10601
It's not everything involved in traditional planning, but it may be a framework to use more traditional planning algorithms on LLM output.
"not a single word about the safety implications of such a system"
Oh please. Not everything has to be regulated-to-hells before a use case is even found on this. Autonomous agents have existed for decades.
If it can automate agents like huginn[0] with natural language, I'd be very happy. Autonomous agents doesn't mean it's going to take over the world autonomously. Let's lower the fearmongering a bit.
[0]: https://github.com/huginn/huginn
I think for agents to truly find adoption in real world, agent trajectory fine tuning is critical component - how do you make an agent perform better to achieve particular objective with every subsequent run. Basically making the agents learn similar to how we learn when we
Also I think current LLMs might not fit well for agent use cases in mid to long term because the RL they go through is based on input-best output methods whereas the intelligence that you need in agents is more around how to build an algorithm to achieve an objective on the fly - this requires perhaps new type of large models ( Large Agent Models ? ) which are trained using RLfD ( Reinforcement Learning from demonstration )
Also I think one of the key missing piece is a highly configurable software middle ware between Intelligence ( LLMs ), Memory ( Vector Dbs ~LTMs, STMs ), Tools and workflows across every iteration. Current agent core loop to find next best action is too simplistic. For example if core self prompting loop or iteration of an agent can be configured for the use case in hand. Eg for BabyAGI, every iteration goes through workflow of Plan, Prioritize and Execute or in AutoGPT it finds the next best action based on LTM/STM, or GPTEngineer it is to write specs > write tests > write code. Now for dev infra monitoring agent this workflow might be totally different - it would look like consume logs from different tools like Grafana, Splunk, APMs > See if it doesnt have an anomaly > if it has an anomaly then take human input for feedback. Every use case in real world has it's own workflow and current construct of agent frameworks have this thing hard coded in base prompt. In SuperAGI( https://superagi.com) ( disclaimer : Im creator of it ), core iteration workflow of agent can be defined as part of agent provisioning.
Another missing piece is notion of Knowledge. Agents currently depend entirely upon knowledge of LLMs or search results to execute on tasks, but if a specialised knowledge set is plugged to an agent, it performs significantly better.