We study decentralized computation of the player-optimal stable matching (POSM) with unannounced player arrivals and departures. Without a common clock, event information, or direct communication, players propose to arms and observe only their own accept/reject outcomes. Mismatch cost counts rounds when the realized matching differs from the current POSM. For known preferences, we give a deterministic polynomial-time self-calibrating protocol with cost $O(t_*+K^2\log T)$, improving the previous bound $O(T^{1-1/K}(\log T)^{2/K})$. Here $K$ is the number of arms, $t_*$ the last event time, and $T$ the horizon. Players use only local time and proposal outcomes, knowing neither $t_*$ nor $T$. For any nondecreasing, polynomial-time-computable $g(t)=Ω(t)$, the protocol achieves $O(g(t_*)+K^2N_g(T))$. Here $g$ governs recovery time, while $N_g(T)$ counts iterations of $t\mapsto t+g(t)$ below $T$, exposing a tradeoff between recovery time and asymptotic mismatch cost. Any randomized protocol with recovery scale $g(t)\ge1$ and sublinear asymptotic mismatch cost has worst-case expected cost $Ω(t_*+N_g(T))$. Thus, with $g(t)=t$, our protocol is optimal up to a $K^2$ factor among sublinear-cost protocols with $O(t_*)$ recovery time. For unknown preferences on both sides, each player and arm learns its preferences from its own observations after matching. We give a decentralized learning protocol with expected cost $O(t_*+K^4Δ_{\mathrm{min}}^{-2}\log T)$, where $Δ_{\mathrm{min}}$ measures preference gaps. A complementary static-setting lower bound adapted from the literature shows that the dependence on $T$ and $Δ_{\mathrm{min}}$ is tight.

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