The idea of devoting more resources to investigating claims when they involve potential costs is involves decision theory rather than just mere prediction.

All claims involve decision theory in the sense that you're presumably going to act on them at some point.

However, vanilla reinforcement learning should handle this OK. Agents that don't investigate extraordinary claims will be exploited and suffer - and a conventional reinforcement learning agent can be expected to pick up on this just fine.

Would these agents also learn to pick up pennies in front of steam rollers? In fact, falling for Pascal's mugging is just the extreme case of refusing to pick up pennies in front of a steam roller, the question is where you draw a line dividing the two.