@inproceedings{zhang-etal-2026-heal,
title = "{HEAL}: Hindsight Entropy-Assisted Learning for Reasoning Distillation",
author = "Zhang, Wenjing and
Yan, Jiangze and
Huang, Jieyun and
Shen, Yi and
Shi, Shuming and
Chen, Ping and
Wang, Ning and
Liu, Zhaoxiang and
Wang, Kai and
Lian, Shiguo",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.355/",
pages = "7430--7443",
ISBN = "979-8-89176-462-0",
abstract = "Distilling reasoning capabilities from Large Reasoning Models (LRMs) into smaller models is typically constrained by the limitations of rejection sampling. Standard methods treat the teacher as a static filter, discarding complex ``corner-case'' problems where the teacher fails to explore valid solutions independently, thereby creating an artificial ``Teacher Ceiling'' for the student. In this work, we propose Hindsight Entropy-Assisted Learning (HEAL), an RL-free framework designed to bridge this reasoning gap. Drawing on the educational theory of the Zone of Proximal Development (ZPD), HEAL synergizes three core modules: (1) Guided Entropy-Assisted Repair (GEAR), an active intervention mechanism that detects critical reasoning breakpoints via entropy dynamics and injects targeted hindsight hints to repair broken trajectories; (2) Perplexity-Uncertainty Ratio Estimator (PURE), a ratio-based filtering heuristic that reduces high-anomaly shortcut-like rationales; and (3) Progressive Answer-guided Curriculum Evolution (PACE), a three-stage distillation strategy that organizes training from foundational alignment to hard-case adaptation. Extensive experiments on multiple benchmarks demonstrate that HEAL significantly outperforms traditional SFT distillation and other baselines."
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<abstract>Distilling reasoning capabilities from Large Reasoning Models (LRMs) into smaller models is typically constrained by the limitations of rejection sampling. Standard methods treat the teacher as a static filter, discarding complex “corner-case” problems where the teacher fails to explore valid solutions independently, thereby creating an artificial “Teacher Ceiling” for the student. In this work, we propose Hindsight Entropy-Assisted Learning (HEAL), an RL-free framework designed to bridge this reasoning gap. Drawing on the educational theory of the Zone of Proximal Development (ZPD), HEAL synergizes three core modules: (1) Guided Entropy-Assisted Repair (GEAR), an active intervention mechanism that detects critical reasoning breakpoints via entropy dynamics and injects targeted hindsight hints to repair broken trajectories; (2) Perplexity-Uncertainty Ratio Estimator (PURE), a ratio-based filtering heuristic that reduces high-anomaly shortcut-like rationales; and (3) Progressive Answer-guided Curriculum Evolution (PACE), a three-stage distillation strategy that organizes training from foundational alignment to hard-case adaptation. Extensive experiments on multiple benchmarks demonstrate that HEAL significantly outperforms traditional SFT distillation and other baselines.</abstract>
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%0 Conference Proceedings
%T HEAL: Hindsight Entropy-Assisted Learning for Reasoning Distillation
%A Zhang, Wenjing
%A Yan, Jiangze
%A Huang, Jieyun
%A Shen, Yi
%A Shi, Shuming
%A Chen, Ping
%A Wang, Ning
%A Liu, Zhaoxiang
%A Wang, Kai
%A Lian, Shiguo
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zhang-etal-2026-heal
%X Distilling reasoning capabilities from Large Reasoning Models (LRMs) into smaller models is typically constrained by the limitations of rejection sampling. Standard methods treat the teacher as a static filter, discarding complex “corner-case” problems where the teacher fails to explore valid solutions independently, thereby creating an artificial “Teacher Ceiling” for the student. In this work, we propose Hindsight Entropy-Assisted Learning (HEAL), an RL-free framework designed to bridge this reasoning gap. Drawing on the educational theory of the Zone of Proximal Development (ZPD), HEAL synergizes three core modules: (1) Guided Entropy-Assisted Repair (GEAR), an active intervention mechanism that detects critical reasoning breakpoints via entropy dynamics and injects targeted hindsight hints to repair broken trajectories; (2) Perplexity-Uncertainty Ratio Estimator (PURE), a ratio-based filtering heuristic that reduces high-anomaly shortcut-like rationales; and (3) Progressive Answer-guided Curriculum Evolution (PACE), a three-stage distillation strategy that organizes training from foundational alignment to hard-case adaptation. Extensive experiments on multiple benchmarks demonstrate that HEAL significantly outperforms traditional SFT distillation and other baselines.
%U https://aclanthology.org/2026.findings-emnlp.355/
%P 7430-7443
Markdown (Informal)
[HEAL: Hindsight Entropy-Assisted Learning for Reasoning Distillation](https://aclanthology.org/2026.findings-emnlp.355/) (Zhang et al., Findings 2026)
ACL
- Wenjing Zhang, Jiangze Yan, Jieyun Huang, Yi Shen, Shuming Shi, Ping Chen, Ning Wang, Zhaoxiang Liu, Kai Wang, and Shiguo Lian. 2026. HEAL: Hindsight Entropy-Assisted Learning for Reasoning Distillation. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 7430–7443, Budapest, Hungary. Association for Computational Linguistics.