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How to Read Paper Efficiently
From dense theory to runnable code — my AI-powered workflow for decoding research and GitHub repos.
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Recently, I was reading a paper, Reinforced Self-play Reasoning with Zero Data, which describes how to train a large language model through reinforcement learning without using human data.
This concept is described in Welcome to the Era of Experience, which states that for an AI agent to be able to exceed human intelligence, it is not enough to rely on human data to learn. The agent must be able to evolve on its own based on the results of its interaction with the environment.
In the past, we have seen that a large part of the improvement in modeling capability comes from human data or human prejudgment.
Human data is better understood as the conversations or formulas that we have prepared in advance to guide the agent to make accurate conclusions. On the other hand, human prejudgment is to tell the agent whether the result is good or bad after the agent has done certain behaviors.
