Parallel ensembles of successive cancellation (SC)-style decoders improve error-rate performance by decoding multiple representations of the received vector. Conventional ensembles, however, keep every member active until completion. To reduce computational cost while retaining the diversity gain of ensemble decoding, we propose early ensemble selection (EES), which ranks members using intermediate decoding states and retains a prescribed numbers of members at each predefined selection stage. Within the EES framework, we propose two selection rules, namely path-metric-based selection (PM-EES) and neural-network-based selection (NN-EES). We analytically characterize the computational cost of EES through stage-dependent decoder log-likelihood ratio (LLR) update counts and provide an information-theoretically motivated criterion for choosing selection stages. Simulations on the \((128,64)\) extended BCH code and the \(\operatorname{RM}(4,8)\) code show that suitable selection schedules approach full-ensemble error-rate performance with fewer decoder LLR updates.

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