aiqbench analysis
This paper introduces Density-Aware Reward Scaling (DARS), a lightweight mechanism designed to mitigate single-mode collapse in reinforcement learning by inversely scaling rewards based on local outcome density. By integrating directly into standard policy-gradient pipelines without requiring auxiliary models or critics, DARS effectively promotes reward-proportional sampling to enhance diversity in tasks ranging from multimodal navigation to text-to-image generation. The approach offers a practical solution for maintaining broad coverage of high-reward solutions within existing RL frameworks.
Density-Aware Reward Scaling: A Reinforcement Learning Framework for Reward-Proportional Sampling
arXiv:2611.04792v1 Announce Type: new Abstract: Reinforcement learning objectives that maximize expected return can concentrate probability mass on a small subset of high-reward outcomes, limiting diversity when multiple distinct solutions are desirable. We introduce Density-Aware Reward Scaling (DARS), a simple reward-processing mechanism that promotes reward-proportional sampling in continuous outcome spaces while retaining standard policy-gradient training. DARS estimates the local density of outcomes within each on-policy rollout group and inversely scales rewards by this density. The method requires no learned density model, auxiliary objective, critic, or modification to the underlying policy-gradient objective. In a controlled multimodal navigation task, DARS-GRPO avoids the single-mode collapse exhibited by GRPO and closely recovers the target mode allocation. Applied to text-to-image prompt adaptation, DARS-GRPO consistently improves image and prompt diversity across datasets, Policy LMs, and reward settings while maintaining good semantic alignment. These results show that density-aware reward scaling provides a lightweight way to improve coverage of diverse high-reward outcomes within existing reinforcement-learning pipelines.
Abstract:Reinforcement learning objectives that maximize expected return can concentrate probability mass on a small subset of high-reward outcomes, limiting diversity when multiple distinct solutions are desirable. We introduce Density-Aware Reward Scaling (DARS), a simple reward-processing mechanism that promotes reward-proportional sampling in continuous outcome spaces while retaining standard policy-gradient training. DARS estimates the local density of outcomes within each on-policy rollout group and inversely scales rewards by this density. The method requires no learned density model, auxiliary objective, critic, or modification to the underlying policy-gradient objective. In a controlled multimodal navigation task, DARS-GRPO avoids the single-mode collapse exhibited by GRPO and closely recovers the target mode allocation. Applied to text-to-image prompt adaptation, DARS-GRPO consistently improves image and prompt diversity across datasets, Policy LMs, and reward settings while maintaining good semantic alignment. These results show that density-aware reward scaling provides a lightweight way to improve coverage of diverse high-reward outcomes within existing reinforcement-learning pipelines.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2611.04792 [cs.LG]
(or arXiv:2611.04792v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2611.04792 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 249 words