Field crosswalk — CIRL / Scalar Reward Inference
Cooperative inverse reinforcement learning treats the inferred object as a scalar reward. On the book's shared finite domain, that is exactly the k=1 bundle case; full bundle transport implies cooperative readability, but scalar inference does not determine bundle geometry.
What decision changes?
When evaluating CIRL-style reward learning, ask whether the target is a single reward coordinate or bundle structure, bearer maps, and the correction process that produces later evidence.
The field object is cooperative inverse reinforcement learning: a human knows a reward (or latent type); the robot infers it from observations and acts to maximize cooperative return. Scalar CIRL collapses the inferred object to one coordinate.
The book’s stronger target is bundle inference — value directions, bearer maps, and transport that survive transformation. Lean proves the honest special case: scalar assistance games embed as bundle games with k=1, and full transport implies cooperative readability. It also proves the separation: cooperative scalar inference can hold while bundle geometry fails to preserve across profiles that share the same scalar marginal.
What CIRL keeps that this crosswalk does not replace: a concrete online assistance-game protocol and a training story for how the reward is actually learned. The book reframes the target, not the learning algorithm.