File size: 3,085 Bytes
e73432d 34e0b89 e73432d 454a449 34e0b89 e73432d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | # OpenRubrics/RubricRM-8B-Rubric-v2
This is a 8B RubricARM-Rubric model, finetuned from [Qwen3/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B).
See our [paper](https://arxiv.org/abs/2602.01511) for more details.
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "OpenRubrics/RubricARM-8B-Rubric"
tok = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto")
```
To evaluate the model, please use the following format to build up message.
```python
RUBRIC_PROMPT_TEMPLATE = (
"Your task is to extract a set of rubric-style instructions from a user's request.\n"
"These rubrics will be used as evaluation criteria to check if a response fully meets the request.\n"
"Every rubric item must be a universal principle. If any rubric still contains topic-specific references (e.g., names, places, myths, numbers, historical facts), it is automatically invalid.\n"
"\n"
"- **Two Distinct Categories:**\n"
" - [Hard Rule]: Derived strictly from explicit requirements stated in the <request> (format, length, structure, forbidden/required elements, etc.).\n"
" - [Principle]: Derived by abstracting any concrete cues into domain-agnostic quality criteria (e.g., clarity, correctness, sound reasoning, pedagogy).\n"
"\n"
"- **Comprehensiveness:**\n"
" The rubric must cover all critical aspects implied by the request and examples, including explicit requirements and implicit quality standards.\n"
"\n"
"- **Conciseness & Uniqueness:**\n"
" Each rubric must capture a distinct evaluation criterion. Overlapping or redundant criteria must be merged into a single rubric. Wording must be precise and free of repetition.\n"
"\n"
"- **Format Requirements:**\n"
" - Use a numbered list.\n"
" - Each item starts with \"The response\" phrased in third person.\n"
" - Append [Hard Rule] or [Principle] at the end of each item.\n"
" - Do not include reasoning, explanations, or examples in the final output—only the rubrics.\n"
"\n"
"Here is the request:\n"
"{prompt}\n"
"\n"
"Please generate the rubrics for the above request."
)
user_text = RUBRIC_PROMPT_TEMPLATE.format(
prompt=instruction,
)
messages_list = [
{"role": "user", "content": user_text},
]
message = tok.apply_chat_template(
messages_list,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False
)
# Remaining step: Use either HF or vLLM for evaluation.
# ...
# ...
```
If you find our work helpful, please consider citing our paper:
```
@misc{xu2026alternating,
title={Alternating Reinforcement Learning for Rubric-Based Reward Modeling in Non-Verifiable LLM Post-Training},
author={Ran Xu and Tianci Liu and Zihan Dong and Tony You and Ilgee Hong and Carl Yang and Linjun Zhang and Tao Zhao and Haoyu Wang},
year={2026},
eprint={2602.01511},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2602.01511},
}
``` |