Multi-Task GRPO: Reliable LLM Reasoning Across Tasks
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.
6985a2734ad556f294b7ed1c
Shyam Sundhar Ramesh
6985a2734ad556f294b7ed1d
Xiaotong Ji
claimed_verified
2026-03-24T09:23:23.839Z
667b6e28a0e1bc140664e725
/avatars/2e4a69a0f98ce6de3f044b2831609ea4.svg
xiaotong
xtongji
user
xtongji
6985a2734ad556f294b7ed1e
Matthieu Zimmer
6985a2734ad556f294b7ed1f
Sangwoong Yoon
6985a2734ad556f294b7ed20
Zhiyong Wang
6985a2734ad556f294b7ed21
Haitham Bou Ammar
6985a2734ad556f294b7ed22
Aurelien Lucchi
6985a2734ad556f294b7ed23
Ilija Bogunovic
Multi-Task GRPO: Reliable LLM Reasoning Across Tasks
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.
Multi-Task GRPO algorithm improves balanced performance across diverse reasoning tasks by dynamically adapting task weights and using a ratio-preserving sampler to ensure equitable optimization.
GRPO multi-task adaptation worst-task performance policy gradients ratio-preserving sampler task weights optimization signal training efficiency