Add metadata and link to paper
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by nielsr HF Staff - opened
README.md
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# OpenRubrics/RubricARM-8B-Judge
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This is a 8B RubricARM-Judge model, finetuned from [Qwen3/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B).
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## Usage
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```python
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JUDGE_PROMPT_TEMPLATE = (
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"You are a fair and impartial judge. Your task is to evaluate 'Response A' and 'Response B' "
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"based on a given instruction and a rubric. You will conduct this evaluation in distinct "
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"phases as outlined below.
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"- **A rule is objective (and likely a Gatekeeper) if it can be verified without opinion. "
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"Key examples are: word/paragraph limits, required output format (e.g., JSON validity), "
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"required/forbidden sections, or forbidden content.**
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"- **Conversely, a rule is subjective if it requires interpretation or qualitative judgment. "
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"Subjective rules about quality are NOT Gatekeepers. Examples include criteria like \"be creative,\" "
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"\"write clearly,\" \"be engaging,\" or \"use a professional tone.\"**
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"Next, for each Gatekeeper Criterion and all other criteria in the rubric, evaluate each "
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"response item by item.
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"### Phase 3: Final Judgment Instructions
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"Based on the results from the previous phases, determine the winner using these simple rules. "
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"Provide a final justification explaining your decision first and then give your decision.
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"--
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)
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user_text = JUDGE_PROMPT_TEMPLATE.format(
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# ...
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# ...
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```
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If you find our work helpful, please consider citing our paper:
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```
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@misc{xu2026alternating,
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title={Alternating Reinforcement Learning for Rubric-Based Reward Modeling in Non-Verifiable LLM Post-Training},
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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},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2602.01511},
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}
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```
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---
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pipeline_tag: text-generation
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library_name: transformers
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base_model: Qwen/Qwen3-8B
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tags:
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- reward-modeling
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- alignment
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- rubric-based-evaluation
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---
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# OpenRubrics/RubricARM-8B-Judge
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This is a 8B RubricARM-Judge model, finetuned from [Qwen3/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B).
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It was introduced in the paper [Alternating Reinforcement Learning for Rubric-Based Reward Modeling in Non-Verifiable LLM Post-Training](https://huggingface.co/papers/2602.01511).
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## Usage
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```python
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JUDGE_PROMPT_TEMPLATE = (
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"You are a fair and impartial judge. Your task is to evaluate 'Response A' and 'Response B' "
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"based on a given instruction and a rubric. You will conduct this evaluation in distinct "
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"phases as outlined below.
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"
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"### Phase 1: Compliance Check Instructions
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"
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"First, identify the single most important, objective 'Gatekeeper Criterion' from the rubric.
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"
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"- **A rule is objective (and likely a Gatekeeper) if it can be verified without opinion. "
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"Key examples are: word/paragraph limits, required output format (e.g., JSON validity), "
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"required/forbidden sections, or forbidden content.**
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"
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"- **Conversely, a rule is subjective if it requires interpretation or qualitative judgment. "
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"Subjective rules about quality are NOT Gatekeepers. Examples include criteria like \"be creative,\" "
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"\"write clearly,\" \"be engaging,\" or \"use a professional tone.\"**
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"
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f"Think step-by-step to determine this single most important Gatekeeper, then write a 1–2 sentence explanation of your decision.
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"
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"### Phase 2: Analyze Each Response
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"
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"Next, for each Gatekeeper Criterion and all other criteria in the rubric, evaluate each "
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"response item by item.
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"
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"For each item, think step-by-step and cite concrete evidence from the response before assigning your judgment.
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"
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"### Phase 3: Final Judgment Instructions
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"
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"Based on the results from the previous phases, determine the winner using these simple rules. "
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"Provide a final justification explaining your decision first and then give your decision.
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"
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"Think step-by-step to aggregate the findings and make the decision; keep the reasoning explicit and concise.
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"
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"---
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"
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"### REQUIRED OUTPUT FORMAT
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"
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"You must follow this exact output format below.
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"
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"--- Compliance Check ---
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"
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"Gatekeeper Reasoning: <1–2 sentences citing the relevant rubric text>
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"
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"Identified Gatekeeper Criterion: <e.g., Criterion 1: Must be under 50 words.>
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"
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"--- Analysis ---
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"
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"**Response A:**
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"
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"- Criterion 1 [Hard Rule]: Justification: <...>
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"
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"- Criterion 2 [Hard Rule]: Justification: <...>
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"
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"- Criterion 3 [Principle]: Justification: <...>
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"
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"- ... (and so on for all other criteria)
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"
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"**Response B:**
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"
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"- Criterion 1 [Hard Rule]: Justification: <...>
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"
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"- Criterion 2 [Hard Rule]: Justification: <...>
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"
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"- Criterion 3 [Principle]: Justification: <...>
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"
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"- ... (and so on for all other criteria)
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"
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"--- Final Judgment ---
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"
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# "Aggregation Summary: <Provide a detailed, step-by-step explanation (3–6 sentences) of how the Gatekeeper and other criteria led to the decision>
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"
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"Aggregation Summary: <1–3 sentences explaining how Gatekeeper and other criteria led to the decision>
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"Justification: <...>
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"
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"Winner: <Response A / Response B>
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"
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"Task to Evaluate:
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"
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"Instruction:
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{instruction}
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"
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"Rubric:
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{rubric}
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"
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"Response A:
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{response_a}
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"Response B:
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{response_b}"
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)
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user_text = JUDGE_PROMPT_TEMPLATE.format(
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# ...
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# ...
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```
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## Citation
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If you find our work helpful, please consider citing our paper:
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```bibtex
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@misc{xu2026alternating,
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title={Alternating Reinforcement Learning for Rubric-Based Reward Modeling in Non-Verifiable LLM Post-Training},
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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},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2602.01511},
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}
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```
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