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Targets approximate Bayes-optimal exploration-exploitation by combining amortized variational belief learning (via S5 state-space models), SMC planning from the \u003ccode\u003etrtpi\u003c/code\u003e codebase, and meta-RL into a unified EM framework. The E-step uses a particle-filter planner with nested importance-sampling weights correcting for mismatch between variational and true beliefs; the M-step distills SMC-improved policies and fits the belief ELBO jointly. Designed for single-GPU throughput with fixed-size batches and hidden-state caching. Preliminary results on gridworld and continuous function optimization show favorable sample- and runtime-efficiency scaling with planning budget over a recurrent PPO (RL²) 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