GD²PO: Mitigating Multi-Reward Conflicts via Group-Dynamic reward-Decoupled Policy Optimization

Researchers introduce GD²PO, a novel reinforcement learning framework designed to address the challenges of multi-dimensional reward optimization in Large Language Models (LLMs), specifically focusing on the mitigation of conflicts between competing objectives.

The Challenge of Multi-Dimensional Reward Optimization

As Large Language Models (LLMs) evolve, post-training reinforcement learning (RL) has shifted toward the use of multi-dimensional rewards. This approach is essential for cultivating comprehensive capabilities, as it allows models to be optimized for diverse metrics—such as helpfulness, safety, and conciseness—simultaneously. However, optimizing for multiple objectives often introduces "reward conflicts," where the gradient updates for one objective may negatively impact the performance of another.

From GDPO to GD²PO

To combat these conflicts, previous methodologies like Group reward-Decoupled Policy Optimization (GDPO) attempted to decompose the overall reward score into independent reward groups. By computing the RL loss separately within each group, GDPO aimed to isolate the influence of different reward signals.

Building upon this foundation, Group-Dynamic reward-Decoupled Policy Optimization (GD²PO) seeks to further refine this process. The framework focuses on the dynamic nature of these rewards to more effectively mitigate conflicts and ensure a more stable convergence during the policy optimization phase.

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