RubricARROW-8B-Rubric/README.md

library_name, pipeline_tag, base_model

library_name	pipeline_tag	base_model
transformers	text-generation	Qwen/Qwen3-8B

OpenRubrics/RubricARROW-8B-Rubric

This is an 8B rubric generation model, introduced in the paper RUBRIC-ARROW: Alternating Pointwise Rubric Reward Modeling for LLM Post-training in Non-verifiable Domains.

It is finetuned from Qwen3/Qwen3-8B.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "OpenRubrics/RubricARROW-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.

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.
# ...
# ...

Citation

If you find our work helpful, please consider citing our paper:

@misc{jiang2026rubric,
      title={RUBRIC-ARROW: Alternating Pointwise Rubric Reward Modeling for LLM Post-training in Non-verifiable Domains}, 
      author={Haoxiang Jiang and Zihan Dong and Tianci Liu and Wanying Wang and Ran Xu and Tony Yu and Linjun Zhang and Haoyu Wang},
      year={2026},
      eprint={2605.29156},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2605.29156}, 
}