The Loop Running End to End

In the described learning system, a frozen model acquires new capabilities through a cycle of detecting tasks it cannot perform, collecting attempts, and compiling successful results into cartridges using Prefix-Tuning methods, while the base model weights remain unchanged. The system begins with an empty library and grows to hold five new capabilities over six cycles, maintaining earlier learned functions even as new ones are added. Each cycle involves detecting missing knowledge, training, and validating the new capability before integrating it into the library, which is tested to ensure no damage to existing functions. The system's process of learning from its own traces involves verifying outputs against a deterministic checker and applying updates to real production traffic, achieving a win-or-tie rate increase from 42% to 59% over seven updates. Despite achieving these updates without performance degradation, the system faces challenges with selection accuracy, which significantly reduces the achievable success rate, highlighting the need for better selection mechanisms to enhance the system's efficiency and reliability.

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