Researchers have published QVal, a framework for evaluating how well dense supervision signals train long-horizon LLM agents—addressing a gap in assessing whether step-by-step feedback actually improves multi-step reasoning versus alternative training approaches. The evaluation methodology matters because dense supervision is computationally expensive to generate and annotate. Without reliable measurement of its ROI, teams risk scaling training costs without proportional capability gains. This directly affects budget allocation for agent development and determines whether intermediate step supervision justifies its overhead versus outcome-only training. For builders, this shifts the baseline evaluation workflow. Instead of assuming dense supervision is beneficial, teams can now empirically measure its contribution to specific reasoning tasks. This makes training cost optimization tractable—organizations can stratify which agent tasks warrant expensive step-level annotation versus cheaper outcome-level feedback. The framework also reduces unnecessary infrastructure investment in annotation pipelines for agents where dense signals provide minimal learning benefit.