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Large language models can generate highly fluent and but inaccurate text. But Reinforcement learning systems can be far more accurate and cost-effective.
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Reinforcement learning systems can be far more accurate and cost-effective than large language models because they learn by doing. Large language models can write code suggestions and so much has been made of their usefulness in unit testing. However, because LLMs trade accuracy for generalization, the best they can do is suggest code to developers, who then must check the code for effectiveness.
