README.md

---
license: apache-2.0
base_model:
- Qwen/Qwen3-VL-8B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- human-simulation
- role-play
- social-intelligence
---

# Ditto-8B

**Ditto-8B** is an 8B open-weight model for **human behavior simulation**, covering theory of
mind, character role-play, social skills, learner simulation, user simulation, and persona
simulation.

- 📄 Paper: [Reinforcing Human Behavior Simulation via Verbal Feedback](https://arxiv.org/abs/2605.20506)
- 💻 Code: https://github.com/sunnweiwei/OdysSim
- 📊 Data (SOUL): https://huggingface.co/datasets/sunweiwei/Soul

## Method

Ditto-8B is trained with **DITTO**, a reinforcement learning method that uses **verbal
feedback** as the learning signal. After each output, the model receives descriptive feedback
and produces an improved version; both are jointly optimized with GRPO. This distills the
verbal guidance into the policy, so **no feedback is needed at inference time**.

## Results

Primary metric for each benchmark (higher is better).

| Dim | Benchmark | GPT 5.5 | Gemini 3.1 Pro | Claude Opus 4.7 | Qwen 3.6 Plus | Others* | Qwen3 8B Inst | Ditto-8B |
|---|---|---|---|---|---|---|---|---|
| CONV | UserLLM | 65.3 | 67.7 | 57.6 | 72.1 | 44.6 | 46.0 | 91.5 |
| CONV | MirrorBench | 56.7 | 48.3 | 63.7 | 48.0 | 45.4 | 54.0 | 73.4 |
| CONV | Humanual-Chat | 28.2 | 21.0 | 22.6 | 22.2 | 25.8 | 24.7 | 21.0 |
| CONV | SimArena-Doc | 83.4 | 83.0 | 83.5 | 82.4 | 83.5 | 83.6 | 84.4 |
| SS | Sotopia-Hard | 31.9 | 27.8 | 32.4 | 28.3 | 31.7 | 27.7 | 45.8 |
| COG | Fantom | 93.0 | 93.0 | 80.0 | 89.0 | 70.0 | 23.0 | 92.0 |
| COG | Hitom | 82.0 | 86.0 | 93.0 | 73.0 | 56.0 | 62.0 | 79.0 |
| COG | Paratomi | 99.0 | 97.0 | 90.0 | 94.0 | 75.0 | 67.0 | 95.0 |
| COG | Social-R1 | 69.0 | 79.0 | 67.0 | 67.0 | 47.0 | 54.0 | 50.0 |
| ROLE | Coser | 66.2 | 62.1 | 66.5 | 55.9 | 30.3 | 43.5 | 64.4 |
| ROLE | Lifechoices | 91.0 | 84.0 | 92.0 | 79.0 | 67.0 | 70.0 | 70.0 |
| ROLE | Twinvoice | 74.0 | 86.0 | 83.0 | 71.0 | 40.0 | 42.0 | 71.0 |
| ROLE | BehaviorChain | 95.0 | 92.0 | 96.0 | 85.0 | 36.0 | 41.0 | 44.0 |
| ROLE | SimArena-Math | 68.5 | 71.5 | 68.7 | 70.9 | 70.5 | 68.9 | 69.6 |
| ROLE | Mistakes | 72.0 | 73.0 | 74.0 | 67.0 | 56.0 | 27.0 | 36.0 |
| ROLE | Humanual-Email | 50.1 | 46.9 | 50.4 | 47.9 | 42.8 | 43.7 | 40.8 |
| ROLE | Humanual-News | 40.2 | 42.3 | 41.3 | 41.8 | 33.1 | 32.5 | 27.5 |
| ROLE | Humanual-Politics | 42.0 | 32.5 | 43.5 | 31.6 | 34.2 | 33.2 | 29.7 |
| EVAL | AlignX | 71.2 | 73.4 | 71.6 | 69.8 | 66.8 | 68.6 | 67.4 |
| EVAL | Humanllm | 45.7 | 46.9 | 44.2 | 42.7 | 35.2 | 34.1 | 33.1 |
| EVAL | Socsci210 | 77.2 | 78.0 | 77.2 | 74.5 | 75.2 | 73.6 | 72.5 |
| EVAL | Humanual-Book | 57.6 | 62.4 | 61.4 | 58.4 | 50.2 | 53.6 | 53.4 |
| EVAL | Humanual-Opinion | 39.8 | 36.0 | 46.2 | 34.2 | 37.4 | 37.2 | 30.3 |

\* *Others*: best result among other specialized human-simulation models (HumanLM-8B, Sotopia-RL-7B, UserLM-8B, Coser-8B).

> **Note.** The released Ditto-8B is a single generalist distilled from a set of task-specific DITTO experts via rejection sampling on the training set.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "sunweiwei/Ditto-8B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")

messages = [{"role": "user", "content": "Hello!"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
```

## Citation

```bibtex
@article{sun2026ditto,
  title         = {Reinforcing Human Behavior Simulation via Verbal Feedback},
  author        = {Sun, Weiwei and Zhou, Xuhui and Liu, Jiarui and Du, Weihua and Sun, Haojia and Xie, Yiqing and Ma, Qianou and Chen, Sihao and Wan, Mengting and Yang, Longqi and Zhou, Pei and Wu, Sherry and Welleck, Sean and Neubig, Graham and Yang, Yiming and Sap, Maarten},
  year          = {2026},
  eprint        = {2605.20506},
  archivePrefix = {arXiv},
  url           = {http://arxiv.org/abs/2605.20506}
}
```