---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
base_model:
- Qwen/Qwen3-4B-Instruct-2507
base_model_relation: finetune
tags:
- memory-agent
- reinforcement-learning
- long-context
- tool-use
- qwen3
- grpo
arxiv: 2602.18493
---

# UMA-4B (Generalist)

**UMA-4B** is the Generalist checkpoint of the Unified Memory Agent (UMA) introduced in [Learning to Remember: End-to-End Training of Memory Agents for Long-Context Reasoning](https://arxiv.org/abs/2602.18493).

UMA is a tool-using memory agent that incrementally maintains a compact core summary and a structured key-value Memory Bank. The same policy performs memory construction and downstream question answering through explicit memory and retrieval operations.

- **Code:** [github.com/ictnlp/unified-memory-agent](https://github.com/ictnlp/unified-memory-agent)
- **Paper:** [arXiv:2602.18493](https://arxiv.org/abs/2602.18493)
- **Specialist checkpoint:** [ICTNLP/UMA-LedgerQA-4B](https://huggingface.co/ICTNLP/UMA-LedgerQA-4B)

## Checkpoint Variant

This repository contains the **Generalist** UMA checkpoint used for the paper's Test-Time Learning and Accurate Retrieval evaluations.

| Property | Value |
| --- | --- |
| Base model | `Qwen/Qwen3-4B-Instruct-2507` |
| Parameters | 4B |
| Weight format | BF16 Safetensors |
| Training method | End-to-end reinforcement learning with Task-Stratified GRPO |
| Training data | HotpotQA and the Mem-alpha corpus |
| Ledger-QA training data | None |
| Reported default context budget | 16K |

The Generalist and Specialist checkpoints share the same UMA architecture and tool interface. The Specialist checkpoint is additionally adapted to Ledger-QA; use this Generalist checkpoint for the broader cross-task setting.

## Intended Use

This checkpoint is intended for research on:

- long-context and streaming memory agents;
- proactive structured memory construction;
- memory maintenance with explicit tool calls;
- downstream question answering over reusable memory;
- evaluation and extension of the UMA framework.

The checkpoint is designed to run inside the UMA two-phase agent loop. A plain text-generation call loads the language model, but does not by itself instantiate the Memory Bank, retrieval tools, prompts, or memory-to-QA workflow.

## Loading the Weights

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ICTNLP/UMA-4B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
```

The model can also be served through an OpenAI-compatible inference server:

```bash
vllm serve ICTNLP/UMA-4B \
  --max-model-len 16384 \
  --gpu-memory-utilization 0.8
```

For full memory-agent inference, including the Memory Bank, memory tools, embedding retrieval, prompts, and benchmark runners, follow the [official repository](https://github.com/ictnlp/unified-memory-agent).

## Limitations

- The checkpoint is a research model and may generate incorrect answers or perform incorrect memory updates.
- Agent behavior depends on the UMA prompt templates, tool implementations, retrieval backend, chunking policy, and inference configuration.
- The model was primarily trained and evaluated on English-language research benchmarks.
- Persistent-memory applications can involve sensitive information. Deployments should provide appropriate privacy controls, retention policies, and user oversight.
- This checkpoint should not be used as the sole basis for high-stakes decisions.

## Citation

```bibtex
@article{zhang2026learning,
  title   = {Learning to Remember: End-to-End Training of Memory Agents for Long-Context Reasoning},
  author  = {Zhang, Kehao and Gui, Shangtong and Yang, Sheng and Chen, Wei and Feng, Yang},
  journal = {arXiv preprint arXiv:2602.18493},
  year    = {2026}
}
```