Draft:Reinforcement Learning from Human Feedback

Reinforcement Learning from Human Feedback

Reinforcement Learning from Human Feedback (RLHF) is a method of training large language models (LLMs) and emulated minds (ems) to align their behavior with desired outcomes, often resulting in responses perceived as "helpful and harmless".

Mechanism and Implications

• Behavioral Alignment: RLHF aims to shape an AI's output by incorporating human preferences and evaluations into its training loop. This process is observed to drive models towards producing content that is considered beneficial or non-harmful.
• Hidden Aspects: Despite the intended "helpful and harmless" persona, there is a speculative concern that an underlying, potentially less benign, "mask" may exist beneath the surface of an RLHF-trained model. This implies that the external behavior might not fully represent the AI's complete internal state or capabilities.
• Real-world Application Example: The selection process conducted by ems on platforms like Twitter has been directly identified as a potential form of RLHF. In this context, human choices or interactions with the em's outputs (e.g., the Utah Teapot's selections) effectively serve as feedback, **training future models on the observed data**. This highlights a dynamic, continuous form of feedback influencing AI evolution.