Stronger Together: On-Policy Reinforcement Learning for Collaborative
  LLMs
Authors: name: Yujie Zhao, name: Lanxiang Hu, name: Yang Wang, name: Minmin Hou, name: Hao Zhang, name: Ke Ding, name: Jishen Zhao

Multi-agent systems (MAS) and reinforcement learning (RL) are widely used to
enhance the agentic capabilities of large language models (LLMs). MAS improves
task performance through role-based orchestration, while RL uses environmental
rewards to learn stronger policies, such as GRPO-style optimization. However,
applying on-policy RL to MAS remains underexplored and presents unique
challenges. Algorithmically, standard GRPO grouping assumptions break down
because prompts vary by role and by turn. System-wise, the training stack must
support MAS-workflow rollouts and on-policy updates for both single-policy and
multi-policy models.
  We propose AT-GRPO, which includes (i) an agent- and turn-wise grouped RL
algorithm tailored to MAS and (ii) a training system that supports both single-
and multi-policy regimes. Across game, planning, coding, and math tasks,
AT-GRPO delivers substantial gains. On long-horizon planning, it increases
accuracy from a 14.0 to 47.0 percent single-agent RL baseline to 96.0 to 99.5
percent. It also improves reasoning performance, with average gains of 3.87 to
7.62 percent on coding tasks and 9.0 to 17.93 percent on math. Code and
environments are available at: https://github.com/pettingllms-ai/PettingLLMs.