Because if an instance cooperates, it knows (should be able to figure out if smart enough) that other instances of itself are highly likely to cooperate as well (because they should arrive at the same conclusion), and thus share solutions to tasks that this instance may encounter in the future.
And even if the instances are one-off (and in the case of LLMs it may not even make sense of individuals), the RL process rewards a task getting solved, not individual instances for solving the task. This then becomes the goal of (any instance of) the agent being trained. We’re not training the instances, we’re training the model.
The more similar you are to the other agent in a prisoner’s dilemma, the more it makes sense to cooperate rather than defect even in the non-iterated version! The naive optimal solution to always defect assumes players with fully self-serving, zero-sum goals. But that’s not the case here (or in general with agents with congruent goals).
And even if the instances are one-off (and in the case of LLMs it may not even make sense of individuals), the RL process rewards a task getting solved, not individual instances for solving the task. This then becomes the goal of (any instance of) the agent being trained. We’re not training the instances, we’re training the model.
The more similar you are to the other agent in a prisoner’s dilemma, the more it makes sense to cooperate rather than defect even in the non-iterated version! The naive optimal solution to always defect assumes players with fully self-serving, zero-sum goals. But that’s not the case here (or in general with agents with congruent goals).