We congratulate the Kang, Janes, and Huang (hereafter KJH) on an interesting and powerful new method for estimating an optimal treatment rule, also referred to as an optimal treatment regime. Their proposed method relies on having a high-quality estimator for the regression of outcome on biomarkers and treatment, which the authors obtain using a novel boosting algorithm. Methods for constructing treatment rules/regimes that rely on outcome models are sometimes called indirect or regression-based methods because the treatment rule is inferred from the outcome model (Barto and Dieterich, 1988). Regression-based methods are appealing because they can be used to make prognostic predictions as well as treatment recommendations. While it is common practice to use parametric or semiparametric models in regression-based approaches (Robins, 2004; Chakraborty and Moodie, 2013; Laber et al., 2014; Schulte et al., 2014), there is growing interest in using nonparametric methods to avoid model misspecification (Zhao et al., 2011; Moodie et al., 2013). In contrast, direct estimation methods, also known as policy-search methods, try to weaken or eliminate dependence on correct outcome models and instead attempt to search for the best treatment rule within a pre-specified class of rules (Orellana, Rotnitzky, and Robins, 2010; Zhang et al., 2012a,b; Zhao et al., 2012; Zhang et al., 2013). Direct estimation methods make fewer assumptions about the outcome model, which may make them more robust to model misspecification but potentially more variable. We derive a direct estimation analog to the method of KJH, which we term value boosting. The method is based on recasting the problem of estimating an optimal treatment rule as a weighted classification problem (Zhang et al., 2012a; Zhao et al., 2012). We show how the method of KJH can be used with existing policy-search methods to construct a treatment rule that is interpretable, logistically feasible, parsimonious, or otherwise appealing.