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Human Learning Is Process‑based; LLMs Learn only From Outcomes

Both humans and LLMs use reinforcement to learn. But the mechanism is not the same. When you learn a climbing move, the feedback is continuous. You fall, adjust your grip, shift weight, feel tension through your body. Your internal model updates in real time, mid-attempt. You know exactly which part of the process failed because you experienced it. LLM reinforcement learning works differently. The model generates a complete response. A human annotator rates it - preferred or not preferred. The model's parameters shift toward outputs that scored higher. The model never experiences the attempt. It has no access to which internal steps contributed to the result. It receives one signal: the final output was good or bad. Human learning is process-based. You update your understanding at every step along the way. LLM learning is outcome-based. Only the end result is evaluated. This distinction matters if you build with these models. A model can produce a correct answer through flawed intermediate reasoning. It can also reason through every step correctly and still arrive at a wrong conclusion. During training, no one rewarded the reasoning process. Only the output. This is why you cannot evaluate an LLM by checking if the final answer looks right. You need to evaluate the reasoning path independently, because the training never did. I go deeper on this in my recent video. Link below.