129 lines
5.6 KiB
Markdown
129 lines
5.6 KiB
Markdown
---
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library_name: transformers
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tags:
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- reward-model
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- prm
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- generative reward model
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- process supervision
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- chain-of-thought
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- verification
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- math reasoning
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- code verification
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license: apache-2.0
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pipeline_tag: text-generation
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---
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# Model Card for ThinkPRM-7B
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ThinkPRM-7B is a generative Process Reward Model (PRM) based on the R1-Distill-Qwen-7B architecture. It is fine-tuned to perform step-by-step verification of reasoning processes (like mathematical solutions) by generating an explicit verification chain-of-thought (CoT) that involves labeling every step. It is designed to be highly data-efficient, requiring significantly less supervision data than traditional discriminative PRMs while achieving strong performance.
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Here's an example of the model output:
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## Model Details
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### Model Description
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ThinkPRM-7B provides step-level verification scores by generating natural language critiques and correctness judgments for each step in a given solution prefix. It leverages the underlying reasoning capabilities of the base Large Reasoning Model (LRM) and enhances them through fine-tuning on a small (1K examples) dataset of synthetically generated verification CoTs. These synthetic CoTs were produced by prompting QwQ-32B-Preview and filtered against ground-truth step labels from the PRM800K dataset to ensure quality.
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The model uses a standard language modeling objective, making it interpretable and allowing it to scale process verification compute by generating longer or multiple verification CoTs. It demonstrated superior performance compared to LLM-as-a-judge and discriminative PRM baselines (based on the same R1-Distill-Qwen-7B model but trained on ~100x more labels) on benchmarks including ProcessBench, MATH-500, AIME '24, GPQA-Diamond, and LiveCodeBench.
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- **Finetuned from model [optional]:** [R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B)
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### Model Sources [optional]
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- **Repository:** [Github](https://github.com/mukhal/thinkprm)
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- **Paper:** [Process Reward Models that Think (arXiv:2504.16828)](https://arxiv.org/abs/2504.16828)
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### Direct Use
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ThinkPRM-7B is intended for verifying the correctness of step-by-step reasoning processes. Primary uses include:
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- **Scoring Solutions:** Assigning step-level or overall scores to candidate solutions for ranking in Best-of-N sampling or guiding tree search in reasoning tasks.
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- **Generating Verification Rationales/CoTs:** Producing detailed chain-of-thought verifications that explain *why* a particular step is correct or incorrect, aiding interpretability.
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- **Standalone Verification:** Evaluating the correctness of a given problem-solution pair.
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The model has been evaluated on mathematical reasoning (MATH, AIME), scientific QA (GPQA), and code generation (LiveCodeBench). See our paper for more details.
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## Limitations
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- **Overconfidence:** Generative PRMs like ThinkPRM can sometimes produce scores clustered near 0 or 1, potentially not reflecting true uncertainty
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- **Step Label Interference:** The autoregressive nature might cause an early incorrect step judgment to negatively bias the evaluation of subsequent steps.
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- **Sensitivity to Formatting/Prompting:** Performance might be sensitive to the exact format of the input solution and the prompt used for verification (though fine-tuning likely reduces this compared to LLM-as-a-judge).
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## How to Get Started with the Model
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from vllm import LLM, SamplingParams
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model_id = "launch/ThinkPRM-7B" # Replace with actual model ID on Hub
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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llm = LLM(model=model_id, max_model_len=16384)
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# Example problem and solution
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problem = "Solve for x: 2x + 3 = 7"
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prefix = "Step 1: Subtract 3 from both sides: 2x = 4
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Step 2: Divide by 2: x = 1"
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# Format the prompt
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prompt = f"""You are given a math problem and a proposed step-by-step solution:
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[Math Problem]
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{problem}
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[Solution]
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{prefix}
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Review and critique each step in the proposed solution to determine whether each step is correct. If the solution is incomplete, only verify the provided steps
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"""
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prompt = tokenizer.apply_chat_template([
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{'role': "user", "content": prompt}
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], tokenize=False, add_generation_prompt=True) + "
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Let's verify step by step:"
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# Set sampling parameters
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sampling_params = SamplingParams(
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temperature=0.0,
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max_tokens=4096,
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stop=None
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)
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# Generate the verification
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outputs = llm.generate(prompt, sampling_params)
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verification_cot = outputs[0].outputs[0].text
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print(verification_cot)
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"""
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Step 1: Subtract 3 from both sides: 2x = 4
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Critique: Starting with the equation 2x + 3 = 7, subtracting 3 from both sides is a correct operation to isolate the term with the variable. So, 2x + 3 - 3 = 7 - 3, which simplifies
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to 2x = 4. This step seems correct.
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Step 2: Divide by 2: x = 1
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Critique: Now, to solve for x, we need to divide both sides of the equation by 2. So, 2x / 2 = 4 / 2, which simplifies to x = 2. Wait a minute, the solution says x = 1, but accordin
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g to this calculation, it should be x = 2. This seems incorrect.
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Therefore, the first step is correct, but the second step has an error.
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**Final Output:**
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Let's verify step by step:
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Step 1: Subtract 3 from both sides: 2x = 4
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Critique: This step is correct. Subtracting 3 from both sides of the equation 2x + 3 = 7 properly isolates the term with the variable, resulting in 2x = 4.
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Step 1 is \boxed{correct}
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Step 2: Divide by 2: x = 1
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Critique: This step is incorrect. Dividing both sides of the equation 2x = 4 by 2 should yield x = 2, not x = 1.
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Step 2 is \boxed{incorrect}
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</think>
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Is the solution correct? No
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""" |