@rahulchhabra07: Introducing RL with Neural Feedback (RLNF). We have found reliable ways to train AI models to respond to human preferen…
Summary
Introducing RL with Neural Feedback (RLNF), a method claiming reliable training of AI models to align with human preferences using only brain signals, demonstrated via a face image that self-transforms into a more attractive version for the viewer.
View Cached Full Text
Cached at: 10/30/26, 11:20 AM
Introducing RL with Neural Feedback (RLNF). We have found reliable ways to train AI models to respond to human preferences purely from brain signals.
The example below is a picture of a face that self-transforms into a more attractive version for the subject as they are looking https://t.co/sBYqPDoVNP
Similar Articles
@heyshrutimishra: We went from "please rate this response 1 to 5" to "don't worry, your brain already told us
A commentary tweet highlighting RL with Neural Feedback (RLNF), a claimed approach to training AI models on human preferences using only brain signals, moving beyond explicit rating-based feedback.
@rohanpaul_ai: this is just so next level for personalized AI. arousal signals scored by a simple embedding plus k-NN reward. RL with …
A tweet discussing RL with Neural Feedback (RLNF), where a viewer's brain arousal signals are scored via embedding plus k-NN and used directly as the RL reward for personalized image generation, framed as a natural evolution beyond RLHF's click-based preferences.
@aisolopreneur: Our dopamine receptors are about to be fried beyond comprehension.
A viral tweet highlights a claimed advance called RL with Neural Feedback (RLNF), where AI models are trained to align with human preferences using direct brain signals, demonstrated via a face image that transforms into a more attractive version in real time as the subject views it.
Learning from human preferences
OpenAI presents a method for training AI agents using human preference feedback, where an agent learns reward functions from human comparisons of behavior trajectories and uses reinforcement learning to optimize for the inferred goals. The approach demonstrates strong sample efficiency, requiring less than 1000 bits of human feedback to train an agent to perform a backflip.
Show HN: I RL-trained an agent that trains models with RL (for –$1.3k)
A developer built a pipeline where an RL-trained AI agent creates and submits RL training jobs for small models, rewarding the agent for better performance. The project is fully open-sourced and demonstrates transfer to held-out tasks.