Hindsight Hint Distillation: Scaffolded Reasoning for SWE Agents from CoT-free Answers
Solving complex long-horizon tasks requires strong planning and reasoning capabilities. Although datasets with explicit chain-of-thought (CoT) rationales can substantially benefit learning, they are costly to obtain. To address this challenge, we propose Hindsight Hint Distillation (HHD), which only requires easy-to-obtain question-answer pairs without CoT annotations. Inspired by how human teachers use student mistakes to provide targeted guidance, HHD synthesizes hindsight hints from the model's own failed self-rollouts and uses them to scaffold on-policy rollouts that successfully complete the tasks. The model then self-distills these scaffolded trajectories and generalizes to new prob...
| Authors | Shengjie Wang, Guanghe Li, Zonghan Yang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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