YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.


license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE language:

  • en pipeline_tag: text-generation base_model: Qwen/Qwen2.5-7B tags:
  • chat

MemAgent β€” agentic/memagent/

Our code hub is :https://github.com/LMIS-ORG/slime-agentic

Reproduces the core idea of MemAgent: compressing arbitrarily long documents into a fixed-size recurrent memory via a chunk-by-chunk LLM update loop, then answering questions from memory alone. RL (GRPO) is applied to all memory-update turns using a Multi-Conversation training objective, so the model learns to retain what matters across chunks without ever seeing the full context at once.

Architecture

Input: question + long document
  β”‚
  β–Ό
memory = "No previous memory"
  β”‚
  └─► for chunk in split(document, chunk_tokens):
        β”‚
        └─ LLM(problem, memory, chunk) β†’ updated memory   (loss_mask=1)
  β”‚
  β–Ό
LLM(problem, memory) β†’ final answer in \boxed{}           (loss_mask=0)
  β”‚
  β–Ό
Reward: exact-match / F1 against ground truth
         (distributed evenly across all memory-update turns)

Each memory-update turn is an independent training sequence. The reward is evenly amortised across all turns in the conversation (via custom_convert), matching the Multi-Conv RL objective in the MemAgent paper.

Results

Evaluated on RULER-HQA across context lengths from 7K to 448K tokens (5 runs, best score reported):

Model 7K 14K 28K 56K 112K 224K 448K
MemAgent (ours) 78.12 76.56 75.78 74.22 77.34 72.66 69.53
QwenLong-L1-32B 72.66 75.00 72.66 60.94 31.25 17.19 13.28
Qwen2.5-Instruct-14B-1M 60.16 60.94 50.00 57.03 50.00 37.50 8.59
Qwen2.5-Instruct-7B-1M 61.72 56.25 53.91 55.47 51.56 33.59 12.50
DS-Distill-Qwen-32B 70.31 66.41 65.62 46.88 23.44 13.28 7.81
DS-Distill-Qwen-14B 64.06 64.84 57.03 40.62 14.84 8.59 3.12
DS-Distill-Qwen-7B 30.47 12.50 3.12 0.00 0.00 0.78 0.00

MemAgent (ours) is trained on a 7B base model and consistently outperforms all baselines, including much larger models, across all context lengths.

Downloads last month
3
Safetensors
Model size
8B params
Tensor type
BF16
Β·
Inference Providers NEW
This model isn't deployed by any Inference Provider. πŸ™‹ Ask for provider support

Paper for LMIS-ORG/MemAgent_Slime_Agentic_Qwen2.5_7B