Deep learning (DL) has shown promise for flow simulations, but progress is constrained by the scarcity of high-fidelity training data, which is costly to generate and limited to few flow conditions. Closures trained offline on such data tend to overfit and fail to generalise to new regimes. We introduce an online optimisation framework for DL-based Reynolds-averaged Navier-Stokes (RANS) closures. Training data is generated by a simulation at direct numerical simulation (DNS) resolution embedded in a subdomain of the RANS domain. The RANS solution supplies boundary conditions to the embedded simulation, which provides the mean velocity and turbulence statistics used to update the closure, which thereby adapts to the target flow without precomputed datasets. The approach is developed on the stochastically forced Burgers equation and on turbulent channel flow at $Re_τ=180$, 270, 395 and 590 with embedded domain lengths $1\leq L_0/L\leq 8$, and is then applied to spanwise-rotating channel flow at $Ro_b=0.15$ and $0.5$, a flow in which the Coriolis force vanishes from the equations of a linear eddy-viscosity closure. Online-optimised RANS models outperform offline-trained and literature-calibrated closures with modest embedded subdomains, and in the rotating channel recover the rotation-induced asymmetry from non-rotating inflow turbulence and a rotation-blind closure. The learned coefficients are interpretable, recovering near-wall damping and, under rotation, the collapse of the eddy viscosity on the stabilised side. Performance degrades when boundary contamination dominates or domains are too short to capture low-wavenumber modes. This framework provides a route to data-adaptive machine learning closures without large precomputed training datasets.

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