DVAO: Dynamic Variance-adaptive Advantage Optimization for Multi-reward Reinforcement Learning
Reinforcement Learning has become a standard paradigm for aligning Large Language Models with human intent and task requirements. While Group Relative Policy Optimization offers an efficient, value-model-free alternative to Proximal Policy Optimization, adapting it to real-world multi-reward settings remains challenging. Standard scalarization practices, such as Reward Combination and Advantage Combination, suffer from significant drawbacks: Reward Combination frequently generates advantages with excessively large squared magnitudes that lead to training instability, while Advantage Combination relies on static hyperparameters and ignores cross-objective correlations. To address these lim...
| Authors | Guochao Jiang, Jingyi Song, Guofeng Quan et al. |
|---|---|
| Primary category | cs.CL |
| Published | 25 May 2026 |
| Last updated at source | 25 May 2026 |
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