Preprint Open access
Make Sparse Rewards Count: Density-Aware Reward Aggregation for Multi-Reward RL
Multi-reward reinforcement learning trains large language models to satisfy multiple behavioral objectives simultaneously. Reward-wise normalization, as used in GDPO, preserves reward-specific relative information within rollout groups, but different objectives can still exhibit uneven learning progress. We study this …