Various risk-limiting audit (RLA) methods have been developed for instant-runoff voting (IRV) elections. A recent method, AWAIRE, is the first efficient approach that can take advantage of but does not require cast vote records (CVRs). AWAIRE involves adaptively weighted averages of test statistics, essentially "learning" an effective set of hypotheses to test. However, the initial paper on AWAIRE only examined a few weighting schemes and parameter settings. We explore schemes and settings more extensively, to identify and recommend efficient choices for practice. We focus on the case where CVRs are not available, assessing performance using simulations based on real election data. The most effective schemes are often those that place most or all of the weight on the apparent "best" hypotheses based on already seen data. Conversely, the optimal tuning parameters tended to vary based on the election margin. Nonetheless, we quantify the performance trade-offs for different choices across varying election margins, aiding in selecting the most desirable trade-off if a default option is needed. A limitation of the current AWAIRE implementation is its restriction to a small number of candidates -- up to six in previous implementations. One path to a more computationally efficient implementation would be to use lazy evaluation and avoid considering all possible hypotheses. Our findings suggest that such an approach could be done without substantially compromising statistical performance.