awesome-RLHF VS visual-chatgpt

Recently, OpenDILab made a paper collection about Reinforcement Learning with Human Feedback (RLHF) and it has been open-sourced on GitHub. This repository is dedicated to helping researchers to collect the latest papers on RLHF, so that they can get to know this area better and more easily.

Welcome to follow and star! https://github.com/opendilab/awesome-RLHF

Here we’re gonna introduce a new repository open-sourced by OpenDILab. Recently, OpenDILab made a paper collection about Reinforcement Learning with Human Feedback (RLHF) and it has been open-sourced on GitHub. This repository is dedicated to helping researchers to collect the latest papers on RLHF, so that they can get to know this area better and more easily. About RLHF Reinforcement Learning with Human Feedback (RLHF) is an extended branch of Reinforcement Learning (RL) that allows the RLHF family of methods to incorporate human feedback into the training process by using this feedback to construct By using this feedback to build a reward model neural network that provides reward signals to help RL intelligences learn, human needs, preferences, and perceptions can be more naturally communicated to the intelligence in an interactive learning manner, aligning the optimization goals between humans and artificial intelligence to produce systems that behave in a manner consistent with human values. Reinforcement Learning with Human Feedback (RLHF) is an extended branch of Reinforcement Learning. When the optimization goal is abstract and it's very difficult to define the specific reward function, RLHF can help to put human feedback into the training process. This feedback can be constructed into a reward neural network model so that RL agents can learn from the given reward signal and naturally convey human needs, preference and attitude to agents through interactive learning.

For prompting, the GitHub repo I refer to the most is actually one from Microsoft: https://github.com/Microsoft/Visual-ChatGPT

Found relevant code at https://github.com/microsoft/visual-chatgpt + all code implementations here

Github: https://github.com/microsoft/visual-chatgpt/tree/main/LowCodeLLM will soon be available!

It's not so much the size of the LLMs, it's what people are able to do with them in novel ways, e.g. https://github.com/microsoft/visual-chatgpt/tree/main/TaskMatrix.AI

There is a python library langchain, which microsoft used to give it access to image editing tools. You can write your own tools for it very easily, I've been having fun with it. I gave it internet search tools and it worked great at using current events in the response.

Something like this: https://github.com/microsoft/visual-chatgpt/tree/main/TaskMatrix.AI could enable a far simpler AI to start making copies of itself.

Reminds me of VisualChatGPT (https://github.com/microsoft/visual-chatgpt), which also uses a LLM to decide what vision models to run.

The best Microsoft API or overall API? It really depends what your goal is. https://github.com/huggingface is great if you want to dive right in, but https://github.com/microsoft/visual-chatgpt might be good as well (not easy to get to work locally).