@inproceedings{fang-etal-2026-allocate,
title = "How to Allocate, How to Learn? Dynamic Rollout Allocation and Advantage Modulation for Policy Optimization",
author = "Fang, Yangyi and
Lin, Jiaye and
Fu, Xiaoliang and
Qin, Cong and
Shi, Haolin and
Hu, Chaowen and
Pan, Lu and
Zeng, Ke and
Cai, Xunliang",
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.724/",
pages = "14727--14744",
ISBN = "979-8-89176-395-1",
abstract = "Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for Large Language Model (LLM) reasoning, yet current methods face key challenges in resource allocation and policy optimization dynamics: (i) uniform rollout allocation ignores gradient variance heterogeneity across problems, and (ii) the softmax policy structure causes gradient attenuation for high-confidence correct actions, while excessive gradient updates may destabilize training. Therefore, we propose DynaMO, a theoretically-grounded dual-pronged optimization framework. At the sequence level, we prove that uniform allocation is suboptimal and derive variance-minimizing allocation from the first principle, establishing Bernoulli variance as a computable proxy for gradient informativeness. At the token level, we develop gradient-aware advantage modulation grounded in theoretical analysis of gradient magnitude bounds. Our framework compensates for gradient attenuation of high-confidence correct actions while utilizing entropy changes as computable indicators to stabilize excessive update magnitudes. Extensive experiments conducted on a diverse range of mathematical reasoning benchmarks demonstrate consistent improvements over strong RLVR baselines. Our implementation is available at: [https://github.com/GithubX-F/DynaMO-RL](https://github.com/FlyTune/DynaMO-RL)."
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<abstract>Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for Large Language Model (LLM) reasoning, yet current methods face key challenges in resource allocation and policy optimization dynamics: (i) uniform rollout allocation ignores gradient variance heterogeneity across problems, and (ii) the softmax policy structure causes gradient attenuation for high-confidence correct actions, while excessive gradient updates may destabilize training. Therefore, we propose DynaMO, a theoretically-grounded dual-pronged optimization framework. At the sequence level, we prove that uniform allocation is suboptimal and derive variance-minimizing allocation from the first principle, establishing Bernoulli variance as a computable proxy for gradient informativeness. At the token level, we develop gradient-aware advantage modulation grounded in theoretical analysis of gradient magnitude bounds. Our framework compensates for gradient attenuation of high-confidence correct actions while utilizing entropy changes as computable indicators to stabilize excessive update magnitudes. Extensive experiments conducted on a diverse range of mathematical reasoning benchmarks demonstrate consistent improvements over strong RLVR baselines. Our implementation is available at: [https://github.com/GithubX-F/DynaMO-RL](https://github.com/FlyTune/DynaMO-RL).</abstract>
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%0 Conference Proceedings
%T How to Allocate, How to Learn? Dynamic Rollout Allocation and Advantage Modulation for Policy Optimization
%A Fang, Yangyi
%A Lin, Jiaye
%A Fu, Xiaoliang
%A Qin, Cong
%A Shi, Haolin
%A Hu, Chaowen
%A Pan, Lu
%A Zeng, Ke
%A Cai, Xunliang
%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 fang-etal-2026-allocate
%X Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for Large Language Model (LLM) reasoning, yet current methods face key challenges in resource allocation and policy optimization dynamics: (i) uniform rollout allocation ignores gradient variance heterogeneity across problems, and (ii) the softmax policy structure causes gradient attenuation for high-confidence correct actions, while excessive gradient updates may destabilize training. Therefore, we propose DynaMO, a theoretically-grounded dual-pronged optimization framework. At the sequence level, we prove that uniform allocation is suboptimal and derive variance-minimizing allocation from the first principle, establishing Bernoulli variance as a computable proxy for gradient informativeness. At the token level, we develop gradient-aware advantage modulation grounded in theoretical analysis of gradient magnitude bounds. Our framework compensates for gradient attenuation of high-confidence correct actions while utilizing entropy changes as computable indicators to stabilize excessive update magnitudes. Extensive experiments conducted on a diverse range of mathematical reasoning benchmarks demonstrate consistent improvements over strong RLVR baselines. Our implementation is available at: [https://github.com/GithubX-F/DynaMO-RL](https://github.com/FlyTune/DynaMO-RL).
%U https://aclanthology.org/2026.findings-acl.724/
%P 14727-14744
Markdown (Informal)
[How to Allocate, How to Learn? Dynamic Rollout Allocation and Advantage Modulation for Policy Optimization](https://aclanthology.org/2026.findings-acl.724/) (Fang et al., Findings 2026)
ACL
- Yangyi Fang, Jiaye Lin, Xiaoliang Fu, Cong Qin, Haolin Shi, Chaowen Hu, Lu Pan, Ke Zeng, and Xunliang Cai. 2026. How to Allocate, How to Learn? Dynamic Rollout Allocation and Advantage Modulation for Policy Optimization. In Findings of the Association for Computational Linguistics: ACL 2026, pages 14727–14744, San Diego, California, United States. Association for Computational Linguistics.