Multi-Turn Reinforcement Learning for Tool-Calling Agents with Iterative Reward Calibration

1. 1Apply MT-GRPO for multi-turn policy optimization
2. 2Utilize GTPO for token-level policy optimization
3. 3Integrate an LLM-based user simulator for realistic customer service tasks
4. 4Implement iterative reward calibration for improved credit assignment

AI engineers and researchers on a team can benefit from this approach to improve the performance of tool-calling agents in multi-turn tasks, while product managers can apply this to enhance customer service experiences

💡 Combining MT-GRPO with GTPO and iterative reward calibration can effectively train tool-calling agents for multi-turn tasks

🤖 Train tool-calling agents with Multi-Turn Reinforcement Learning & iterative reward calibration for better customer service!