ahmedehabb/Memory-R2
ahmedehabb/Memory-R2 is a 7.6 billion parameter Qwen2.5-7B-Instruct model fine-tuned as a memory-management policy for long-horizon memory-augmented LLM agents. Developed by Sikuan Yan et al., it is specifically designed to decide what to insert, update, or delete in an external memory store during a conversation. This model's unique function is to manage memory, not to generate answers, making it a specialized component for complex agentic systems.
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Overview
Memory-R2 is a specialized 7.6 billion parameter Qwen2.5-7B-Instruct model, developed by Sikuan Yan et al., functioning as a memory-management policy for long-horizon memory-augmented LLM agents. Unlike typical LLMs, its sole purpose is to manage an external memory store by deciding what information to INSERT, UPDATE, or DELETE during a running conversation. It does not answer questions directly; instead, it works in conjunction with a separate answer agent (such as the ahmedehabb/Memory-R2-answer-agent) that reads the memory store it produces.
Key Capabilities
- Memory Management: Optimizes the content of an external memory store for LLM agents.
- Long-Horizon Dialogue: Trained specifically for managing memory across extended conversational contexts.
- Flexible Integration: Designed to be paired with any instruction-tuned LLM as an answer agent.
Training Details
- Base Model: Qwen2.5-7B-Instruct.
- Algorithm: Fine-tuned using LoGo-GRPO (turn-level + token-level advantage) with a curriculum of 8, 16, and 32-session rollouts.
- Dataset: Trained on the LoCoMo long-horizon dialogue dataset.
- Reward System: Utilizes a per-session cumulative F1 score against gold QA, combined with a memory-compression penalty.
Performance Highlights
When paired with various answer agents, Memory-R2 consistently contributes to strong performance in memory-augmented dialogue systems. For instance, with the SFT+RL trained answer agent, it achieves an F1 score of 51.46 and a BLEU-1 of 44.84.