Multi-Task GRPO: Reliable LLM Reasoning Across Tasks
This addresses the need for reliable LLM reasoning across diverse tasks, which is crucial for real-world deployment, though it is an incremental improvement over existing methods.
The paper tackles the problem of imbalanced performance when adapting GRPO to multiple reasoning tasks, proposing MT-GRPO with dynamic task weighting and a ratio-preserving sampler to improve worst-task accuracy. It achieves 16-28% absolute improvement over standard GRPO and 50% fewer training steps to reach 50% worst-task accuracy in a 3-task setting.
RL-based post-training with GRPO is widely used to improve large language models on individual reasoning tasks. However, real-world deployment requires reliable performance across diverse tasks. A straightforward multi-task adaptation of GRPO often leads to imbalanced outcomes, with some tasks dominating optimization while others stagnate. Moreover, tasks can vary widely in how frequently prompts yield zero advantages (and thus zero gradients), which further distorts their effective contribution to the optimization signal. To address these issues, we propose a novel Multi-Task GRPO (MT-GRPO) algorithm that (i) dynamically adapts task weights to explicitly optimize worst-task performance and promote balanced progress across tasks, and (ii) introduces a ratio-preserving sampler to ensure task-wise policy gradients reflect the adapted weights. Experiments on both 3-task and 9-task settings show that MT-GRPO consistently outperforms baselines in worst-task accuracy. In particular, MT-GRPO achieves 16-28% and 6% absolute improvement on worst-task performance over standard GRPO and DAPO, respectively, while maintaining competitive average accuracy. Moreover, MT-GRPO requires 50% fewer training steps to reach 50% worst-task accuracy in the 3-task setting, demonstrating substantially improved efficiency in achieving reliable performance across tasks.