Memory-R2 7B — Memory Manager
The trained memory-management policy from Memory-R2: Fair Credit Assignment for Long-Horizon Memory-Augmented LLM Agents (arXiv:2605.21768). This is the paper's main contribution and deployed "champion" (32sess_champion_v2, LoGo-GRPO curriculum, global step 5).
It is a Qwen2.5-7B-Instruct model fine-tuned with LoGo-GRPO (turn-level + token-level credit assignment) via a curriculum of 8 → 16 → 32-session rollouts on the LoCoMo long-horizon dialogue dataset. Given a running conversation, it decides what to INSERT / UPDATE / DELETE in an external memory store.
This model only manages memory — it does not answer questions. A separate answer agent reads the memory store this model produces and generates answers; it can be any instruction-tuned LLM. Our own SFT+RL-trained answer agent is released separately at ahmedehabb/Memory-R2-answer-agent.
Headline results (tab:main)
This memory manager is held constant; only the paired answer agent changes:
| Answer agent | F1 | BLEU-1 | LLM-judge (gpt-4o-mini) |
|---|---|---|---|
| ahmedehabb/Memory-R2-answer-agent (ours, SFT+RL) | 51.46 | 44.84 | 69.03 |
| GPT-OSS-120B (untrained, external) | 49.29 | 43.64 | 86.08 |
See the paper's tab:different-answer-agent for more pairings (untrained Qwen-7B, etc.) — the memory manager is not tied to any one answer agent.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
memory_manager = AutoModelForCausalLM.from_pretrained("ahmedehabb/Memory-R2", torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("ahmedehabb/Memory-R2")
Full inference code and the memory-store protocol are in the project repository (see the paper for the official release).
Training
- Base model:
Qwen/Qwen2.5-7B-Instruct - Algorithm: LoGo-GRPO (turn-level + token-level advantage), curriculum-trained 8-session → 16-session → 32-session
- Reward: per-session cumulative F1 against gold QA + a memory-compression penalty (λ=0.3)
- Judge for reward/logging during training: GPT-OSS-120B
Citation
@misc{yan2026memoryr2faircreditassignment,
title={Memory-R2: Fair Credit Assignment for Long-Horizon Memory-Augmented LLM Agents},
author={Sikuan Yan and Ahmed Bahloul and Ercong Nie and Susanna Schwarzmann and Riccardo Trivisonno and Volker Tresp and Yunpu Ma},
year={2026},
eprint={2605.21768},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2605.21768},
}