How this term is used here In this channel's material on new AI architectures and coding agents, a reward function is the specification of what counts as success for a system being trained. Rather than being shown every correct move, the system explores and is evaluated against the reward function, which tells it how well its behavior matches the desired outcome. Why it matters The reward function is a key piece of the story the channel explores about agents like Devin: verifiable tasks — such as whether code compiles and passes tests — can be turned into a measurable signal, which makes it possible to train and evaluate agents on problems where correctness can be checked rather than guessed. - A reward function defines, in measurable terms, what success looks like for a trained system.- It lets a system explore and improve against an objective instead of needing explicit step-by-step supervision.- It is especially relevant for coding agents, where outcomes like passing tests are verifiable.