PPO, GRPO, DAPO, GDPO Compared for Training LLM Agents on Multi-Reward Tasks

Training large language model agents for real-world enterprise tasks requires balancing multiple competing reward signals — such as task success, execution efficiency, and safety constraints — rather than a single scalar score. Simply combining these reward channels into standard algorithms like PPO or vanilla GRPO often causes training instability, where high-variance rewards drown out subtler signals. PPO's actor-critic architecture also imposes significant hardware costs, requiring a second model of equal size to serve as the critic. Newer methods like GRPO, DAPO, and GDPO attempt to address these issues through group-relative advantage estimation and decoupled channel normalization. An empirical study compares seven trainer configurations on Qwen3-14B across these approaches to evaluate their effectiveness on multi-reward agent post-training.