SCAD: Structured Credit Assignment and Distillation for Long-Horizon Agents

Excerpt

arXiv:2610.03372v3 Announce Type: replace Abstract: Training long-horizon agents to solve complex tasks requires effective supervision over extended interaction sequences. However, sparse terminal rewards obscure intermediate contributions, while on-policy distillation can lose informative teacher guidance as student-generated histories grow. To address this problem, we introduce SCAD, which organizes interactions into planning and bounded subtask execution, distills execution in local contexts,