Hindsight Hint Distillation: Scaffolded Reasoning for SWE Agents from CoT-free Answers

2026-05-12

AI Summary

This work addresses the challenge of enhancing agents’ planning and reasoning capabilities in complex, long-horizon tasks without access to explicit chain-of-thought (CoT) annotations. The authors propose Hindsight Hint Distillation (HHD), a method that leverages only question-answer pairs to synthesize hindsight hints from failed autoregressive trajectories, thereby guiding the generation of successful reasoning paths. By integrating self-distillation, HHD enables cross-task generalization and, for the first time, synthesizes expert-level reasoning trajectories without requiring CoT supervision. The approach demonstrates substantial improvements on out-of-distribution tasks, particularly excelling in multilingual settings. Empirical results show an absolute 8% gain on SWE-bench Verified—significantly surpassing baseline improvements of approximately 2%—and achieves the largest performance gains on the unseen-language SWE-bench Multilingual benchmark.

Abstract

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 problems without hint guidance. Experiments show that HHD significantly outperforms iterative RFT and trajectory-synthesis baselines, achieving an absolute improvement of 8\% on SWE-bench Verified, while all baselines improve by only around 2\%. Notably, the reasoning strategies induced by HHD generalize effectively to out-of-distribution tasks, yielding the largest gains on SWE-bench Multilingual despite no training on multilingual data. These results demonstrate that HHD can effectively synthesize expert-like reasoning from CoT-free data and substantially improve long-horizon performance.

Problem

• long-horizon tasks
• chain-of-thought
• reasoning
• data efficiency
• scaffolded reasoning

Innovation

• Hindsight Hint Distillation
• Chain-of-Thought-free Learning
• Self-Distillation
• Scaffolded Reasoning
• Long-horizon Task Planning