DVAO: Dynamic Variance-adaptive Advantage Optimization for Multi-reward Reinforcement Learning
- Published
- Source
- arXiv
- Paper number
- 250
- Field
- LLMs / NLP
- arXiv ID
- 2605.25604
Key points
- In terms of usability, the algorithm adaptively finds the optimal balance from data, reducing the need for practitioners to manually tune reward weights.
- In terms of stability, it preserves the efficiency of the GRPO framework while adding the theoretical stability needed to avoid size explosion.
- In terms of synergy, cross-objective regularization ensures that the model does not optimize only the easiest reward while sacrificing harder and more important goals such as reasoning accuracy.
Paper links
External research summaries. These are not HDATF publications or measured product results.