ReGRPO: Reflection-Augmented Policy Optimization for Tool-Using Agents
📰 ArXiv cs.AI
Learn how ReGRPO enhances policy optimization for tool-using agents with reflection-augmented methods, improving robustness and recovery from tool failures
Action Steps
- Implement ReGRPO to augment policy optimization with reflection mechanisms
- Use ReGRPO to analyze and recover from tool failures in multimodal tasks
- Compare the performance of ReGRPO with existing supervised fine-tuning and sparse trajectory-level RL rewards methods
- Apply ReGRPO to real-world scenarios involving tool-using agents
- Evaluate the effectiveness of ReGRPO in improving the robustness of tool-augmented vision-language models
Who Needs to Know This
Researchers and engineers working on multimodal, multi-step tasks with tool-augmented vision-language models can benefit from this approach to improve the robustness of their agents
Key Insight
💡 ReGRPO addresses the limitations of existing methods by providing a more informative signal for recovery after tool failures, leading to improved robustness and performance
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💡 Introducing ReGRPO: Reflection-Augmented Policy Optimization for Tool-Using Agents, enhancing robustness and recovery from tool failures #AI #RL
Key Takeaways
Learn how ReGRPO enhances policy optimization for tool-using agents with reflection-augmented methods, improving robustness and recovery from tool failures
Full Article
Title: ReGRPO: Reflection-Augmented Policy Optimization for Tool-Using Agents
Abstract:
arXiv:2606.31392v1 Announce Type: new Abstract: Tool-augmented vision-language models (VLMs) can solve multimodal, multi-step tasks by calling external tools, yet they remain fragile in practice. Existing works have two common gaps. Supervised fine-tuning (SFT) is built mostly on successful trajectories and offers little signal for recovery after tool failures, while sparse trajectory-level RL rewards provide limited guidance on which step failed and how to repair it. We introduce ReGRPO (Reflec
Abstract:
arXiv:2606.31392v1 Announce Type: new Abstract: Tool-augmented vision-language models (VLMs) can solve multimodal, multi-step tasks by calling external tools, yet they remain fragile in practice. Existing works have two common gaps. Supervised fine-tuning (SFT) is built mostly on successful trajectories and offers little signal for recovery after tool failures, while sparse trajectory-level RL rewards provide limited guidance on which step failed and how to repair it. We introduce ReGRPO (Reflec
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