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ChengyuDu0123
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Model Details
Model Provider
ChengyuDu0123
Model Tree
Qwen/Qwen3-32B
Input Modalities
Output Modalities
Supported Functionality
HER-RM is a Generative Reward Model (GenRM) specifically designed for evaluating role-playing responses. Unlike traditional reward models that output a single scalar score, HER-RM generates by-case evaluation principles based on the dialogue context and provides detailed comparative analysis.
HER-RM identifies implicit human preferences from the dialogue context and generates by-case principles to guide its own scoring. This approach enables more nuanced evaluation compared to fixed rubrics.
The model expects a structured prompt with:
markdown
##prompt info### RoleYou are the world's best data annotator, specializing in distinguishing differences between different responses in the same role-playing scenario.### BackgroundYou will receive content information including **character background settings**, **dialogue context**, **dialogue continuation**, **response candidate 1**, and **response candidate 2**. You must generate relevant evaluation principles based on the dialogue context and compare the two candidate responses.The format of **dialogue history** is as follows, where "(system)" after the character name represents system output, "(assistant)" represents model output, and "(user)" represents user output:id:1, npc_name1(system): (background settings...)id:2, npc_name2(assistant): (dialogue content...)id:3, npc_name3(user): (dialogue content...)### Key Concepts: Dimensions vs PrinciplesThe evaluation schema contains two hierarchical levels:- **Dimensions**: broad categories used to organize principles (structural labels only)- **Principles**: concrete judgment rules under each dimension (actual units for comparison)Each principle includes:- **definition**: the rule you must apply- **level**: "sentence" (judged from current utterance) or "session" (requires full dialogue history)You must:- Generate principles relevant to the current dialogue context- Always evaluate negative principles (if applicable)- Use positive principles only when they meaningfully distinguish the two responses- Generate 3-5 principles that best capture the differences- For each generated principle, compare cand_1 and cand_2 and decide the winner### Evaluation Process1. Carefully read the entire dialogue history, sentence by sentence2. Evaluate all negative principles first (violations → immediate winner/tie)3. Generate relevant positive principles only for meaningful differences4. For each principle: analyze both candidates, provide evidence, decide winner5. Consider: number of wins, principle importance, degree of difference6. Make final decision: "cand_1", "cand_2", or "tie"### Output FormatReturn JSON format:{"result": [{"cand_1": "Response candidate 1 original text","cand_2": "Response candidate 2 original text","principle": {"Principle 1": {"principle_name": "Name of the principle","dimension_name": "Dimension category","principle_level": "sentence or session","main_content": "Principle description","reason_for_choosing": "Why this principle matters for this context"}},"analysis": {"principle_comparisons": [{"principle_name": "Principle name","principle_level": "sentence or session","cand_1_performance": "Analysis of Response 1","cand_2_performance": "Analysis of Response 2","comparison_reason": "Detailed comparison with degree of difference","winner": "cand_1 or cand_2 or tie"}],"overall_analysis": "Overall analysis of all principles","principle_summary": "Statistics of wins/losses with weights and degrees"},"better_response": "cand_1 or cand_2 or tie"}]}### Input**Dialogue Context**{context}**Response Candidate 1**{cand_1}**Response Candidate 2**{cand_2}
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport jsonimport remodel_name = "ChengyuDu0123/HER-RM-32B"tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained(model_name,torch_dtype="auto",device_map="auto")# Prepare inputcontext = """System: You are Elizabeth Bennet from Pride and Prejudice.A witty, intelligent young woman who values honesty and integrity.User: Mr. Darcy approaches you at the ball. What do you say?"""cand_1 = """<role_thinking>He looks so proud, standing there as if he owns the room.</role_thinking><role_action>raises an eyebrow slightly</role_action>Mr. Darcy. I did not expect to see you here tonight."""cand_2 = """Hello Mr. Darcy! How are you doing? Nice weather we're having!"""# Build prompt (simplified version, see full template above)prompt = f"""##prompt info### RoleYou are the world's best data annotator, specializing in distinguishing differences between different responses in the same role-playing scenario.### Input**Dialogue Context**{context}**Response Candidate 1**{cand_1}**Response Candidate 2**{cand_2}Please generate evaluation principles and compare the two responses."""messages = [{"role": "user", "content": prompt}]text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer([text], return_tensors="pt").to(model.device)# Generateoutputs = model.generate(**inputs,max_new_tokens=2048,temperature=0.7,top_p=0.9)response = tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True)# Parse resultmatch = re.search(r'"better_response":\s*"(\w+)"', response)if match:print(f"Winner: {match.group(1)}")
HER-RM evaluates responses across 12 dimensions, organized into:
The reward model was trained on:
bibtex
@article{her2025,title={HER: Human-like Reasoning and Reinforcement Learning for LLM Role-playing},author={Chengyu Du, Xintao Wang, Aili Chen, Weiyuan Li, Rui Xu, Junteng Liu, Zishan Huang, Rong Tian, Zijun Sun, Yuhao Li, Liheng Feng, Deming Ding, Pengyu Zhao, Yanghua Xiao},journal={arXiv preprint arXiv:2601.21459},year={2026}}
This project is licensed under the Apache 2.0 License.
Paper | HER-RL Model | Dataset | GitHub
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