@inproceedings{hu-etal-2026-rewarding,
title = "Rewarding the Rare: Uniqueness-Aware {RL} for Creative Problem Solving in {LLM}s",
author = "Hu, Zhiyuan and
Wang, Yucheng and
He, Yufei and
Wu, Jiaying and
Zhao, Yilun and
Ng, See-Kiong and
Breazeal, Cynthia and
Luu, Anh Tuan and
Park, Hae Won and
Hooi, Bryan",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1982/",
pages = "39765--39790",
ISBN = "979-8-89176-395-1",
abstract = "Reinforcement learning (RL) has become a central paradigm for post-training large language models (LLMs), particularly for complex reasoning tasks, yet it often suffers from exploration collapse: policies prematurely concentrate on a small set of dominant reasoning patterns, improving pass@1 while limiting rollout-level diversity and gains in pass@k. We argue that this failure stems from regularizing local token behavior rather than diversity over sets of solutions. To address this, we propose Uniqueness-Aware Reinforcement Learning, a rollout-level objective that explicitly rewards correct solutions that exhibit rare high-level strategies. Our method uses an LLM-based judge to cluster rollouts for the same problem according to their high-level solution strategies, ignoring superficial variations, and reweights policy advantages inversely with cluster size. As a result, correct but novel strategies receive higher rewards than redundant ones. Across mathematics, physics, and medical reasoning benchmarks, our approach consistently improves pass@k across large sampling budgets and increases the area under the pass@k curve (AUC@K) without sacrificing pass@1, while sustaining exploration and uncovering more diverse solution strategies at scale. Code is in Software part under submission page."
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<abstract>Reinforcement learning (RL) has become a central paradigm for post-training large language models (LLMs), particularly for complex reasoning tasks, yet it often suffers from exploration collapse: policies prematurely concentrate on a small set of dominant reasoning patterns, improving pass@1 while limiting rollout-level diversity and gains in pass@k. We argue that this failure stems from regularizing local token behavior rather than diversity over sets of solutions. To address this, we propose Uniqueness-Aware Reinforcement Learning, a rollout-level objective that explicitly rewards correct solutions that exhibit rare high-level strategies. Our method uses an LLM-based judge to cluster rollouts for the same problem according to their high-level solution strategies, ignoring superficial variations, and reweights policy advantages inversely with cluster size. As a result, correct but novel strategies receive higher rewards than redundant ones. Across mathematics, physics, and medical reasoning benchmarks, our approach consistently improves pass@k across large sampling budgets and increases the area under the pass@k curve (AUC@K) without sacrificing pass@1, while sustaining exploration and uncovering more diverse solution strategies at scale. Code is in Software part under submission page.</abstract>
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%0 Conference Proceedings
%T Rewarding the Rare: Uniqueness-Aware RL for Creative Problem Solving in LLMs
%A Hu, Zhiyuan
%A Wang, Yucheng
%A He, Yufei
%A Wu, Jiaying
%A Zhao, Yilun
%A Ng, See-Kiong
%A Breazeal, Cynthia
%A Luu, Anh Tuan
%A Park, Hae Won
%A Hooi, Bryan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F hu-etal-2026-rewarding
%X Reinforcement learning (RL) has become a central paradigm for post-training large language models (LLMs), particularly for complex reasoning tasks, yet it often suffers from exploration collapse: policies prematurely concentrate on a small set of dominant reasoning patterns, improving pass@1 while limiting rollout-level diversity and gains in pass@k. We argue that this failure stems from regularizing local token behavior rather than diversity over sets of solutions. To address this, we propose Uniqueness-Aware Reinforcement Learning, a rollout-level objective that explicitly rewards correct solutions that exhibit rare high-level strategies. Our method uses an LLM-based judge to cluster rollouts for the same problem according to their high-level solution strategies, ignoring superficial variations, and reweights policy advantages inversely with cluster size. As a result, correct but novel strategies receive higher rewards than redundant ones. Across mathematics, physics, and medical reasoning benchmarks, our approach consistently improves pass@k across large sampling budgets and increases the area under the pass@k curve (AUC@K) without sacrificing pass@1, while sustaining exploration and uncovering more diverse solution strategies at scale. Code is in Software part under submission page.
%U https://aclanthology.org/2026.findings-acl.1982/
%P 39765-39790
Markdown (Informal)
[Rewarding the Rare: Uniqueness-Aware RL for Creative Problem Solving in LLMs](https://aclanthology.org/2026.findings-acl.1982/) (Hu et al., Findings 2026)
ACL
- Zhiyuan Hu, Yucheng Wang, Yufei He, Jiaying Wu, Yilun Zhao, See-Kiong Ng, Cynthia Breazeal, Anh Tuan Luu, Hae Won Park, and Bryan Hooi. 2026. Rewarding the Rare: Uniqueness-Aware RL for Creative Problem Solving in LLMs. In Findings of the Association for Computational Linguistics: ACL 2026, pages 39765–39790, San Diego, California, United States. Association for Computational Linguistics.