Multi Agent Training Grpo

Multi-Agentic Training with GRPO Algorithm

**By:** [Fareed Khan](https://medium.com/@fareedkhandev)

Agentic systems for long-horizon tasks require **planning**, correct **tool use**, and **step-by-step execution**. Most modern agentic systems rely on inference, so the model sees all components fresh each time and **lacks prior training**. This increases the chances of **wrong planning** or incorrect tool calls at any step in long-horizon tasks. The **GRPO algorithm**, a modern RL approach, continuously **trains agents to plan** and execute correctly for extended tasks. A typical GRPO-based agentic training system looks like this…

 *Multi-Agentic System with GRPO Algorithm (Created by [Fareed Khan](https://medium.com/@fareedkhandev))*

**How GRPO Affects Agentic Training:**

1. Group-Based Evaluation: GRPO evaluates multiple trajectories for the same query, allowing the agent to compare strategies rather than relying on single-step rewards.
2. Relative Advantage Learning: Successful trajectories are reinforced relative to the group average, improving the probability of correct planning and execution.
3. Error Suppression: Poor trajectories receive negative advantage signals, reducing hallucinations and incorrect tool usage.
4. Iterative Refinement: The agent continuously improves through repeated rollouts, learning to plan long-horizon tasks more reliably.
5. Coordination Across Sub-Agents: By training in a group context, GRPO helps multiple sub-agents align their actions, improving overall multi-agent system performance.

In this blog …

We are going to **learn and understand how the GRPO algorithm is related to AI Agents**, and then **create a multi-agentic system and train it using GRPO**.

Table of Content

• Role of GRPO Algorithm in Agent System
• [Agentic Data Pre