Anyone here know where we can find more resources on RLHF?
There’s been a lot written about transformer models etc., but I wasn’t able to find much about RLHF.
There’s been a lot written about transformer models etc., but I wasn’t able to find much about RLHF.
Webinar on the same topic (from same HF folks): https://www.youtube.com/watch?v=2MBJOuVq380&t=496s
RLHF as used by OpenAI in InstructGPT (predecessor to ChatGPT): https://arxiv.org/abs/2203.02155 (academic paper, so much denser than the above two resources)
But we talk of 'hallucinations' but what we wont get is AI malfeasense identified by AI RLHF trickery/lying?
GANs pair a generative model with a classification model (both unsupervised) whose loss functions have been designed to be antithetical. Basically, one performing well means the other is performing poorly. Keeping with the example posed by the given link, this results in a kind of hyper-optimization that causes the generative model to gradually hone in on the perfect way to render a face, while the classification model keeps pace with it and feeds back that "I don't see a face" until something resembling a face emerges. With this approach, you can start with complete noise and end up at a photorealistic face.
You might've been more correct to say that diffusion surpassed prior generative models, but the adversarial element doesn't even compare to diffusion at all. The adversarial element would be more accurately seen as a trade-off for standard RLHF/Human-in-the-Loop models.
I will bet money that GANs bolstered with diffusion will far outperform a standalone diffusion model.
The key idea is that they're able to start with large amounts of relatively garbage unsupervised data (the internet), and use that model to cheaply generate decent amounts of better data (ranking generated content rather than spending the man-hours to actually write good content). The other details aren't too important.
1. https://www.assemblyai.com/blog/the-full-story-of-large-lang... (you can scroll to "What RLHF actually does to an LLM" if you're already familiar with LLMs)
2. https://www.assemblyai.com/blog/how-chatgpt-actually-works/