The article discusses the current limitations of AI coding agents, which, despite showing some improvement, achieve only a 48.4% success rate in practical tasks. Researchers identify that AI models struggle due to insufficient training data on sequential decision-making and associated behaviors. Future steps include fine-tuning models to improve bug resolution, potentially through smaller models guiding larger ones. While there's promise in the research, experts believe the ideal scenario involves AI assisting human developers rather than replacing them entirely, as many existing outputs contain bugs and vulnerabilities.
This approach is much more successful than relying on the models as they're usually used, but when your best case is a 48.4 percent success rate, you're not ready for primetime.
We believe this is due to the scarcity of data representing sequential decision-making behavior (e.g., debugging traces) in the current LLM training corpus.
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