80 lines
3.0 KiB
Markdown
80 lines
3.0 KiB
Markdown
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---
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license: mit
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base_model: Qwen/Qwen3-1.7B
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tags:
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- empathy
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- reinforcement-learning
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- grpo
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- dialogue
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- mint
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- emotional-support
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language:
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- en
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pipeline_tag: text-generation
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---
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# MINT-empathy-Qwen3-1.7B
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This model is the **Q + D_KL** MINT checkpoint fine-tuned from [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) for multi-turn empathic dialogue.
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MINT, short for **Multi-turn Inter-tactic Novelty Training**, is a reinforcement learning framework that optimizes empathic response quality together with cross-turn discourse-move novelty. At the 1.7B scale, this checkpoint provides the strongest aggregate empathy performance among the MINT variants reported in the paper.
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## Key Results
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On the Lend-an-Ear test set reported in the paper, which contains 315 supporter turns across 50 conversations:
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1. Aggregate empathy improves from **3.60** to **4.54** relative to the vanilla Qwen3-1.7B baseline.
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2. In Table 2 of the paper, tactic stickiness is **0.51** for both the vanilla baseline and this checkpoint.
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3. This checkpoint should therefore be understood primarily as the strongest **1.7B empathy checkpoint**, while the clearest reduction in tactic repetition appears at the 4B scale.
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## Training Summary
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|---|---|
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| **Method** | GRPO via [VERL](https://github.com/volcengine/verl) |
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| **Reward** | Empathy quality + cross-turn tactic diversity |
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| **Base model** | [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) |
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| **KL coeff** | 0.01 |
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| **Diversity weight** | 1.0 |
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| **Response length** | 2048 tokens |
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| **Rollouts** | n=8 per prompt |
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("hongli-zhan/MINT-empathy-Qwen3-1.7B")
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tokenizer = AutoTokenizer.from_pretrained("hongli-zhan/MINT-empathy-Qwen3-1.7B")
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```
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With vLLM:
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```python
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from vllm import LLM
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llm = LLM(model="hongli-zhan/MINT-empathy-Qwen3-1.7B")
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```
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## Intended Use and Limitations
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This model is intended for research on empathic dialogue, discourse diversity, and supportive response generation. It is a research artifact, not a therapy system, and at the 1.7B scale the main improvement is empathy quality rather than a substantial reduction in tactic repetition.
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## Related Artifacts
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- Paper: [Discourse Diversity in Multi-Turn Empathic Dialogue](https://arxiv.org/abs/2604.11742)
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- Project page: [honglizhan.github.io/mint-empathy](https://honglizhan.github.io/mint-empathy/)
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- Code: [github.com/honglizhan/mint-empathy](https://github.com/honglizhan/mint-empathy)
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- Tactic taggers: [hongli-zhan/empathy-tactic-taggers-llama3.1-8b](https://huggingface.co/hongli-zhan/empathy-tactic-taggers-llama3.1-8b)
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- Larger checkpoint: [hongli-zhan/MINT-empathy-Qwen3-4B](https://huggingface.co/hongli-zhan/MINT-empathy-Qwen3-4B)
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## Citation
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```bibtex
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@article{zhan2026discourse,
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title={Discourse Diversity in Multi-Turn Empathic Dialogue},
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author={Zhan, Hongli and Gueorguieva, Emma S and Hernandez, Javier and Suh, Jina and Ong, Desmond C and Li, Junyi Jessy},
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journal={arXiv preprint arXiv:2604.11742},
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year={2026}
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}
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```
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