Counterfactual Evidence-Aware Credit with Matched Controls for Single-Step Reinforcement Learning of GUI Visual Grounding

A GUI grounding policy can click the right location for the wrong visual reason. A binary click reward cannot tell whether the policy relies on the referred interface element or on a shortcut, and editing only the target cannot tell either, because a click can move after any image edit. We introduce counterfactual evidence-aware credit with matched controls (CEAC), which applies the same edit to the target and to same-size control regions outside it and turns the difference in the policy's response into training credit. We measure the matched gap: how much more the click moves when the target is edited than when the controls are edited. Compared with five other training objectives, CEAC increases the matched gap on held-out mobile screens by 15% to 22%, with no statistically significant accuracy loss. On PC/Web, CEAC also has the largest gap, and its lead over three of the five other objectives is significant after multiple-comparison correction. Mask and blur edits give the same gain, which comes from a larger response to target edits while control responses stay flat. Matched controls can thus train a policy to rely on evidence at the target location, which the task reward alone does not require.