Model name: NewBeeKing/MemPO_Qwen2.5-SFT-RL

This model is the reinforcement learning (RL) optimized version of NewBeeKing/MemPO_Qwen2.5-SFT, trained using the MemPO algorithm on the NewBeeKing/MemPO_RL-train-dataset.

MemPO is designed for long-horizon agent tasks, where the interaction history with the environment can grow rapidly and hurt both performance and stability. Instead of relying only on external memory retrieval, MemPO enables the policy model itself to proactively summarize, retain, and manage memory during interaction.

By improving credit assignment based on memory effectiveness, MemPO helps the agent keep crucial information while discarding less useful context, leading to much better token efficiency without sacrificing task performance.

Long-horizon agents face the challenge of growing context size during interaction with environment, which degrades performance and stability. We propose the self-memory policy optimization algorithm (MemPO), which enables the agent to autonomously summarize and manage their memory during interaction. By improving the credit assignment mechanism, the policy model can selectively retain crucial information, significantly reducing token consumption while preserving task performance. Extensive experiments confirm that MemPO achieves absolute F1 score gains of 25.98% over the base model while reducing token usage by up to 73.12%.