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[2602.19208] How to Allocate, How to Learn? Dynamic Rollout Allocation and Advantage Modulation for Policy Optimization
Summary
This article presents DynaMO, a novel framework for optimizing reinforcement learning with verifiable rewards, addressing key challenges in resource allocation and policy dynamics.
Why It Matters
The research is significant as it tackles critical inefficiencies in reinforcement learning methods, particularly in large language models. By improving resource allocation and stabilizing training dynamics, DynaMO can enhance the performance of AI systems in complex reasoning tasks, making it relevant for both academic research and practical applications in AI development.
Key Takeaways
- DynaMO optimizes reinforcement learning by addressing gradient variance and allocation inefficiencies.
- The framework introduces variance-minimizing allocation based on theoretical principles.
- Gradient-aware advantage modulation helps stabilize training by compensating for gradient attenuation.
- Extensive experiments show DynaMO's consistent improvements over existing RLVR baselines.
- The implementation is accessible for further research and application in AI systems.
Computer Science > Machine Learning arXiv:2602.19208 (cs) [Submitted on 22 Feb 2026] Title:How to Allocate, How to Learn? Dynamic Rollout Allocation and Advantage Modulation for Policy Optimization Authors:Yangyi Fang, Jiaye Lin, Xiaoliang Fu, Cong Qin, Haolin Shi, Chaowen Hu, Lu Pan, Ke Zeng, Xunliang Cai View a PDF of the paper titled How to Allocate, How to Learn? Dynamic Rollout Allocation and Advantage Modulation for Policy Optimization, by Yangyi Fang and 8 other authors View PDF HTML (experimental) 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...
