MemPO: Self-Memory Policy Optimization for Long-Horizon Agents
arXiv:2603.00680v1 Announce Type: new Abstract: Long-horizon agents face the challenge of growing context size during interaction with environment, which degrades the performance and stability. Existing methods typically introduce the external memory module and look up the relevant information from the stored memory, which prevents the model itself from proactively managing its memory content and aligning with the agent's overarching task objectives. To address these limitations, we propose the self-memory policy optimization algorithm (MemPO), which enables the agent (policy model) to autonomously summarize and manage their memory during interaction with environment. By improving the credit assignment mechanism based on memory effectiveness, the policy model can selectively retain crucial information, significantly reducing token consumption while preserving task performance. Extensive experiments and analyses confirm that MemPO achieves absolute F1 score gains of 25.98% over the bas
arXiv:2603.00680v1 Announce Type: new Abstract: Long-horizon agents face the challenge of growing context size during interaction with environment, which degrades the performance and stability. Existing methods typically introduce the external memory module and look up the relevant information from the stored memory, which prevents the model itself from proactively managing its memory content and aligning with the agent's overarching task objectives. To address these limitations, we propose the self-memory policy optimization algorithm (MemPO), which enables the agent (policy model) to autonomously summarize and manage their memory during interaction with environment. By improving the credit assignment mechanism based on memory effectiveness, the policy model can selectively retain crucial information, significantly reducing token consumption while preserving task performance. Extensive experiments and analyses confirm that MemPO achieves absolute F1 score gains of 25.98% over the base model and 7.1% over the previous SOTA baseline, while reducing token usage by 67.58% and 73.12%.
Executive Summary
The article proposes MemPO, a self-memory policy optimization algorithm for long-horizon agents. MemPO enables agents to autonomously manage their memory, improving performance and stability. It achieves significant gains in F1 score and reduces token usage, outperforming existing methods. The algorithm's ability to selectively retain crucial information is key to its success, making it a promising approach for long-horizon tasks.
Key Points
- ▸ MemPO enables autonomous memory management for long-horizon agents
- ▸ The algorithm improves credit assignment based on memory effectiveness
- ▸ MemPO achieves significant F1 score gains and reduces token usage
Merits
Improved Performance
MemPO's ability to selectively retain crucial information improves task performance and stability
Efficient Memory Management
The algorithm reduces token consumption while preserving task performance
Demerits
Limited Context
The article does not provide a comprehensive analysis of the limitations and potential biases of the MemPO algorithm
Expert Commentary
The MemPO algorithm represents a significant advancement in autonomous memory management for long-horizon agents. By improving credit assignment and selectively retaining crucial information, MemPO achieves impressive gains in performance and efficiency. However, further research is needed to fully understand the limitations and potential biases of the algorithm. As the field of AI continues to evolve, the development of MemPO highlights the importance of efficient memory management and its potential applications in real-world tasks.
Recommendations
- ✓ Further research into the limitations and potential biases of the MemPO algorithm
- ✓ Exploration of MemPO's applications in real-world tasks, such as natural language processing and robotics