@inproceedings{liu-etal-2026-dual-uncertainty,
title = "Dual-Uncertainty Guided Policy Learning for Multimodal Reasoning",
author = "Liu, Rui and
Yu, Dian and
Zheng, Tong and
Dai, Runpeng and
Li, Zongxia and
Yu, Wenhao and
Liang, Zhenwen and
Song, Linfeng and
Mi, Haitao and
Tokekar, Pratap and
Yu, Dong",
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.2289/",
pages = "49054--49069",
ISBN = "979-8-89176-462-0",
abstract = "Reinforcement learning with verifiable rewards (RLVR) has advanced reasoning capabilities in multimodal large language models. However, existing methods typically treat visual inputs as deterministic, overlooking the perceptual ambiguity inherent to the visual modality. Consequently, they fail to distinguish whether a model{'}s uncertainty stems from complex reasoning or ambiguous perception, preventing the targeted allocation of exploration or learning signals. To address this gap, we introduce $\textbf{DUPL}$, a dual-uncertainty guided policy learning approach for multimodal RLVR that quantifies and leverages both perceptual uncertainty (via symmetric KL divergence) and output uncertainty (via policy entropy) to guide policy updates. By establishing an uncertainty-driven feedback loop and employing a dynamic branch prioritization mechanism, DUPL recalibrates the policy advantage to focus learning on states with high perceptual or decisional ambiguity, enabling effective targeted exploration beyond passive data augmentation. Evaluated on diverse multimodal reasoning benchmarks spanning mathematical and general domains, DUPL achieves solid gains. It improves Qwen2.5-VL accuracy by up to 12.3{\%} (3B) and 7.9{\%} (7B), and Qwen3-VL-Instruct by up to 10.7{\%} (4B) and 12.4{\%} (8B), consistently outperforming GRPO, while seamlessly generalizing to alternative algorithms (DAPO, +6.5{\%} avg) and architectures (LLaVA-OneVision-1.5, +4.7{\%} avg). These results demonstrate that DUPL is an effective and generalizable approach for multimodal RLVR."
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<abstract>Reinforcement learning with verifiable rewards (RLVR) has advanced reasoning capabilities in multimodal large language models. However, existing methods typically treat visual inputs as deterministic, overlooking the perceptual ambiguity inherent to the visual modality. Consequently, they fail to distinguish whether a model’s uncertainty stems from complex reasoning or ambiguous perception, preventing the targeted allocation of exploration or learning signals. To address this gap, we introduce DUPL, a dual-uncertainty guided policy learning approach for multimodal RLVR that quantifies and leverages both perceptual uncertainty (via symmetric KL divergence) and output uncertainty (via policy entropy) to guide policy updates. By establishing an uncertainty-driven feedback loop and employing a dynamic branch prioritization mechanism, DUPL recalibrates the policy advantage to focus learning on states with high perceptual or decisional ambiguity, enabling effective targeted exploration beyond passive data augmentation. Evaluated on diverse multimodal reasoning benchmarks spanning mathematical and general domains, DUPL achieves solid gains. It improves Qwen2.5-VL accuracy by up to 12.3% (3B) and 7.9% (7B), and Qwen3-VL-Instruct by up to 10.7% (4B) and 12.4% (8B), consistently outperforming GRPO, while seamlessly generalizing to alternative algorithms (DAPO, +6.5% avg) and architectures (LLaVA-OneVision-1.5, +4.7% avg). These results demonstrate that DUPL is an effective and generalizable approach for multimodal RLVR.</abstract>
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%0 Conference Proceedings
%T Dual-Uncertainty Guided Policy Learning for Multimodal Reasoning
%A Liu, Rui
%A Yu, Dian
%A Zheng, Tong
%A Dai, Runpeng
%A Li, Zongxia
%A Yu, Wenhao
%A Liang, Zhenwen
%A Song, Linfeng
%A Mi, Haitao
%A Tokekar, Pratap
%A Yu, Dong
%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 liu-etal-2026-dual-uncertainty
%X Reinforcement learning with verifiable rewards (RLVR) has advanced reasoning capabilities in multimodal large language models. However, existing methods typically treat visual inputs as deterministic, overlooking the perceptual ambiguity inherent to the visual modality. Consequently, they fail to distinguish whether a model’s uncertainty stems from complex reasoning or ambiguous perception, preventing the targeted allocation of exploration or learning signals. To address this gap, we introduce DUPL, a dual-uncertainty guided policy learning approach for multimodal RLVR that quantifies and leverages both perceptual uncertainty (via symmetric KL divergence) and output uncertainty (via policy entropy) to guide policy updates. By establishing an uncertainty-driven feedback loop and employing a dynamic branch prioritization mechanism, DUPL recalibrates the policy advantage to focus learning on states with high perceptual or decisional ambiguity, enabling effective targeted exploration beyond passive data augmentation. Evaluated on diverse multimodal reasoning benchmarks spanning mathematical and general domains, DUPL achieves solid gains. It improves Qwen2.5-VL accuracy by up to 12.3% (3B) and 7.9% (7B), and Qwen3-VL-Instruct by up to 10.7% (4B) and 12.4% (8B), consistently outperforming GRPO, while seamlessly generalizing to alternative algorithms (DAPO, +6.5% avg) and architectures (LLaVA-OneVision-1.5, +4.7% avg). These results demonstrate that DUPL is an effective and generalizable approach for multimodal RLVR.
%U https://aclanthology.org/2026.findings-emnlp.2289/
%P 49054-49069
Markdown (Informal)
[Dual-Uncertainty Guided Policy Learning for Multimodal Reasoning](https://aclanthology.org/2026.findings-emnlp.2289/) (Liu et al., Findings 2026)
ACL
- Rui Liu, Dian Yu, Tong Zheng, Runpeng Dai, Zongxia Li, Wenhao Yu, Zhenwen Liang, Linfeng Song, Haitao Mi, Pratap Tokekar, and Dong Yu. 2026. Dual-Uncertainty Guided Policy Learning for Multimodal Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 49054–49069, Budapest, Hungary. Association for Computational Linguistics.