Definition. Agentic post-training state [ftip-004U]

Let \(\mathcal M_{\rm exec}\) be the space of executable model artifacts, \(\mathcal Z_{\rm opt}\) the optimizer-state space, and \(\mathcal R_{\rm ag}\) a declared space of typed agentic round records. At training round \(n\), an agentic post-training state is a tuple \[ \Sigma _n=(M_n,s_n,\mathcal L_n) \in \mathcal M_{\rm exec}\times \mathcal Z_{\rm opt} \times \mathcal R_{\rm ag}^{n}, \] where \(M_n\) is the executable model artifact, \(s_n\) is the optimizer state, and \(\mathcal L_n=(r_0,\ldots ,r_{n-1})\) is an append-only training ledger.

The round-record schema records the task and instance draw, policy and inference stamp, environment version, stopped trajectory, feedback and credit outputs, update decision, proposed and committed next artifact and optimizer state, random seeds, operator versions, and realized cost vector. The tuple separates executable state from optimizer state and from evidence about how that state was reached.