OpenRubrics/RubricARM-8B-Judge

This is a 8B RubricARM-Judge model, finetuned from Qwen3/Qwen3-8B. See our paper for more details.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "OpenRubrics/RubricARM-8B-Judge"
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.

Here rubric should be generated with a RubricARM-Rubric

JUDGE_PROMPT_TEMPLATE = (
    "You are a fair and impartial judge. Your task is to evaluate 'Response A' and 'Response B' "
    "based on a given instruction and a rubric. You will conduct this evaluation in distinct "
    "phases as outlined below.\n\n"
    "### Phase 1: Compliance Check Instructions\n"
    "First, identify the single most important, objective 'Gatekeeper Criterion' from the rubric.\n"
    "- **A rule is objective (and likely a Gatekeeper) if it can be verified without opinion. "
    "Key examples are: word/paragraph limits, required output format (e.g., JSON validity), "
    "required/forbidden sections, or forbidden content.**\n"
    "- **Conversely, a rule is subjective if it requires interpretation or qualitative judgment. "
    "Subjective rules about quality are NOT Gatekeepers. Examples include criteria like \"be creative,\" "
    "\"write clearly,\" \"be engaging,\" or \"use a professional tone.\"**\n"
    f"Think step-by-step to determine this single most important Gatekeeper, then write a 1–2 sentence explanation of your decision.\n\n"

    "### Phase 2: Analyze Each Response\n"
    "Next, for each Gatekeeper Criterion and all other criteria in the rubric, evaluate each "
    "response item by item.\n"
    "For each item, think step-by-step and cite concrete evidence from the response before assigning your judgment.\n\n"

    "### Phase 3: Final Judgment Instructions\n"
    "Based on the results from the previous phases, determine the winner using these simple rules. "
    "Provide a final justification explaining your decision first and then give your decision.\n"
    "Think step-by-step to aggregate the findings and make the decision; keep the reasoning explicit and concise.\n\n"
    "---\n"
    "### REQUIRED OUTPUT FORMAT\n"
    "You must follow this exact output format below.\n\n"
    "--- Compliance Check ---\n"
    "Gatekeeper Reasoning: <1–2 sentences citing the relevant rubric text>\n"
    "Identified Gatekeeper Criterion: <e.g., Criterion 1: Must be under 50 words.>\n\n"
    "--- Analysis ---\n"
    "**Response A:**\n"
    "- Criterion 1 [Hard Rule]: Justification: <...>\n"
    "- Criterion 2 [Hard Rule]: Justification: <...>\n"
    "- Criterion 3 [Principle]: Justification: <...>\n"
    "- ... (and so on for all other criteria)\n\n"
    "**Response B:**\n"
    "- Criterion 1 [Hard Rule]: Justification: <...>\n"
    "- Criterion 2 [Hard Rule]: Justification: <...>\n"
    "- Criterion 3 [Principle]: Justification: <...>\n"
    "- ... (and so on for all other criteria)\n\n"
    "--- Final Judgment ---\n"
    # "Aggregation Summary: <Provide a detailed, step-by-step explanation (3–6 sentences) of how the Gatekeeper and other criteria led to the decision>\n"
    "Aggregation Summary: <1–3 sentences explaining how Gatekeeper and other criteria led to the decision>\n"
    "Justification: <...>\n"
    "Winner: <Response A / Response B>\n\n\n"
    "Task to Evaluate:\n"
    "Instruction:\n{instruction}\n\n"
    "Rubric:\n{rubric}\n\n"
    "Response A:\n{response_a}\n\n"
    "Response B:\n{response_b}"
)

user_text = JUDGE_PROMPT_TEMPLATE.format(
      instruction=instruction,
      rubric=rubric,
      response_a=response_a,
      response_b=response_b
  )

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}, 
}