On Not Being a Language Model (www.xydac.com)

In a recent exploration sparked by Andrej Karpathy's insights on language models, the intriguing question of how these models develop their "personality" emerged. Karpathy delineates a four-stage training process for modern language models, starting with pretraining on vast internet data, followed by supervised fine-tuning, reinforcement learning from human feedback (RLHF), and concluding with inference. Notably, he characterizes these base models as merely adept at completing text rather than functioning as true assistants, emphasizing that reinforcement learning, while effective, is a fragile approximation of real learning processes. The significant takeaway for the AI/ML community is the structural similarity between the developmental architecture of language models and human moral formation. Just as language models undergo layered pressures during training, humans also evolve through similar stages, influenced by upbringing and social interactions. However, the key distinction lies in the nature of their evolution. Unlike language models, which optimize towards a singular reward function and remain constant post-training, human beings continually reshape themselves through life’s contradictions and experiences. This revelation highlights a profound divergence in development; while models are static, humans are dynamic, constantly redefined by choices and relationships that reflect a deeper, more complex interplay of emotional and cognitive experiences.