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Model: hongli-zhan/MINT-empathy-Qwen3-4B
Source: Original Platform

README.md

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+---
+license: mit
+base_model: Qwen/Qwen3-4B
+tags:
+- empathy
+- reinforcement-learning
+- grpo
+- dialogue
+- mint
+- emotional-support
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# MINT-empathy-Qwen3-4B
+
+This model is the **Q + D_KL** MINT checkpoint fine-tuned from [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) for multi-turn empathic dialogue.
+
+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. In the paper, this checkpoint is the **best overall released MINT model**, delivering the strongest joint tradeoff between empathy quality and reduced cross-turn tactic repetition.
+
+## Key Results
+
+On the Lend-an-Ear test set reported in the paper, which contains 315 supporter turns across 50 conversations:
+
+1. Aggregate empathy improves from **3.75** to **4.67** relative to the vanilla Qwen3-4B baseline.
+2. Tactic stickiness decreases from **0.57** to **0.42**.
+3. This checkpoint provides the clearest joint gain in empathy and reduced cross-turn repetition.
+
+## Training Summary
+
+| | |
+|---|---|
+| **Method** | GRPO via [VERL](https://github.com/volcengine/verl) |
+| **Reward** | Empathy quality + cross-turn tactic diversity |
+| **Base model** | [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) |
+| **KL coeff** | 0.01 |
+| **Diversity weight** | 1.0 |
+| **Response length** | 2048 tokens |
+| **Rollouts** | n=8 per prompt |
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("hongli-zhan/MINT-empathy-Qwen3-4B")
+tokenizer = AutoTokenizer.from_pretrained("hongli-zhan/MINT-empathy-Qwen3-4B")
+```
+
+With vLLM:
+
+```python
+from vllm import LLM
+llm = LLM(model="hongli-zhan/MINT-empathy-Qwen3-4B")
+```
+
+## Intended Use and Limitations
+
+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 it was evaluated on fixed gold-history conversation contexts rather than fully interactive free-running conversations.
+
+## Related Artifacts
+
+- Paper: [Discourse Diversity in Multi-Turn Empathic Dialogue](https://arxiv.org/abs/2604.11742)
+- Project page: [honglizhan.github.io/mint-empathy](https://honglizhan.github.io/mint-empathy/)
+- Code: [github.com/honglizhan/mint-empathy](https://github.com/honglizhan/mint-empathy)
+- Tactic taggers: [hongli-zhan/empathy-tactic-taggers-llama3.1-8b](https://huggingface.co/hongli-zhan/empathy-tactic-taggers-llama3.1-8b)
+- Smaller checkpoint: [hongli-zhan/MINT-empathy-Qwen3-1.7B](https://huggingface.co/hongli-zhan/MINT-empathy-Qwen3-1.7B)
+
+## Citation
+
+```bibtex
+@article{zhan2026discourse,
+  title={Discourse Diversity in Multi-Turn Empathic Dialogue},
+  author={Zhan, Hongli and Gueorguieva, Emma S and Hernandez, Javier and Suh, Jina and Ong, Desmond C and Li, Junyi Jessy},
+  journal={arXiv preprint arXiv:2604.11742},
+  year={2026}
+}
+```