MINT-empathy-Qwen3-4B

MINT-empathy-Qwen3-4B

This model is the Q + D_KL MINT checkpoint fine-tuned from 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

Usage

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:

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
• Project page: honglizhan.github.io/mint-empathy
• Code: github.com/honglizhan/mint-empathy
• Tactic taggers: hongli-zhan/empathy-tactic-taggers-llama3.1-8b
• Smaller checkpoint: hongli-zhan/MINT-empathy-Qwen3-1.7B

Citation

@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}
}