Multi-Turn Reinforcement Learning for Tool-Calling Agents with Iterative Reward Calibration
📰 ArXiv cs.AI
Researchers apply Multi-Turn Reinforcement Learning with iterative reward calibration to train tool-calling agents for customer service tasks
Action Steps
- Apply MT-GRPO for multi-turn policy optimization
- Utilize GTPO for token-level policy optimization
- Integrate an LLM-based user simulator for realistic customer service tasks
- Implement iterative reward calibration for improved credit assignment
Who Needs to Know This
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
Key Insight
💡 Combining MT-GRPO with GTPO and iterative reward calibration can effectively train tool-calling agents for multi-turn tasks
Share This
🤖 Train tool-calling agents with Multi-Turn Reinforcement Learning & iterative reward calibration for better customer service!
Key Takeaways
Researchers apply Multi-Turn Reinforcement Learning with iterative reward calibration to train tool-calling agents for customer service tasks
Full Article
Title: Multi-Turn Reinforcement Learning for Tool-Calling Agents with Iterative Reward Calibration
Abstract:
arXiv:2604.02869v1 Announce Type: new Abstract: Training tool-calling agents with reinforcement learning on multi-turn tasks remains challenging due to sparse outcome rewards and difficult credit assignment across conversation turns. We present the first application of MT-GRPO (Multi-Turn Group Relative Policy Optimization) combined with GTPO (Generalized Token-level Policy Optimization) for training a tool-calling agent on realistic customer service tasks with an LLM-based user simulator. Throu
Abstract:
arXiv:2604.02869v1 Announce Type: new Abstract: Training tool-calling agents with reinforcement learning on multi-turn tasks remains challenging due to sparse outcome rewards and difficult credit assignment across conversation turns. We present the first application of MT-GRPO (Multi-Turn Group Relative Policy Optimization) combined with GTPO (Generalized Token-level Policy Optimization) for training a tool-calling agent on realistic customer service tasks with an LLM-based user simulator. Throu
DeepCamp AI