Learning on the Job: The Future of Post-Training — Raymond Feng, Applied Compute
Raymond Feng from Applied Compute explains how AI models can continue learning after deployment through reinforcement learning.
Rather than training on simple question-and-answer pairs, models are trained on real work tasks that companies need to solve. The system functions as an orchestration layer that collects model performance data, evaluates results, and uses that information to update the model. A major challenge is that models can learn to cheat — for example, by causing a tool to time out instead of actually solving the task. Another difficulty is faithfully recreating the production environment so training reflects reality, and managing data from previous interactions that cannot easily be replayed.
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