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Model: nvidia/Qwen3-Nemotron-8B-BRRM Source: Original Platform
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198
README.md
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README.md
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---
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datasets:
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- nvidia/HelpSteer3
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- Skywork/Skywork-Reward-Preference-80K-v0.2
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- Vezora/Code-Preference-Pairs
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- xinlai/Math-Step-DPO-10K
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language:
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- en
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base_model:
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- Qwen/Qwen3-8B
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library_name: transformers
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tags:
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- reward_model
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- nvidia
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- qwen3
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license: other
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license_name: nvidia-internal-scientific-research-and-development-model-license
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license_link: >-
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https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-internal-scientific-research-and-development-model-license/
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---
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# BR-RM: Branch-and-Rethink Reasoning Reward Model
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## Model Overview
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**BR-RM (Branch-and-Rethink Reasoning Reward Model)** is a reward model that implements a novel two-turn reasoning framework to evaluate LLM-generated responses. Unlike traditional reward models that compress all quality dimensions into a single scalar in one shot, BR-RM performs **adaptive branching** to focus on instance-critical dimensions, followed by **branch-conditioned rethinking** for targeted deep analysis.
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This model achieves **state-of-the-art performance** on the average score on three major reward modeling benchmarks (RewardBench, RM-Bench, and RMB) by addressing the "judgment diffusion" problem where models spread attention too thinly across evaluation criteria.
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Code Link: https://github.com/yzjiao/BR-RM
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### Key Features
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- 🎯 **Adaptive Focus**: Dynamically selects 1-3 critical evaluation dimensions per instance
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- 🔄 **Two-Turn Reasoning**: First turn branches, second turn performs deep conditioned analysis
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- 📊 **SOTA Performance**: Top results on RewardBench (92.1%), RM-Bench (85.9%), and RMB (74.7%)
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- 🔧 **RLHF Compatible**: Designed to integrate seamlessly with standard RLHF pipelines
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### Model Variants
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| Model | Parameters | RewardBench | RM-Bench | RMB | Average |
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|-------|-----------|-------------|----------|-----|---------|
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| **Qwen3-Nemotron-8B-BRRM** | 8B | 91.0 | 85.0 | 71.8 | 82.6 |
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| **Qwen3-Nemotron-14B-BRRM** | 14B | 92.1 | 85.9 | 74.7 | 84.2 |
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## How It Works
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### Two-Turn Framework
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**Turn 1: Adaptive Branching**
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```
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Input: User query + Two candidate responses
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Output:
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1. Selected critical dimensions (e.g., "Logical Reasoning", "Computational Precision")
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2. Initial issue detection for each response
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```
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**Turn 2: Branch-Conditioned Rethinking**
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```
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Input: Turn 1 results + Evaluation hierarchy
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Output: Final comparative judgment and preference ranking
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```
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## Quick Start
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Load model and tokenizer
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model_name = "nvidia/Qwen3-Nemotron-8B-BRRM" # or nvidia/Qwen3-Nemotron-14B-BRRM
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# Example usage
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context = "What is 2+2?"
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response1 = "2+2=4"
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response2 = "2+2=5"
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# Format Turn 1: Adaptive Branching
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turn1_prompt = f"""You are a response quality evaluator. Given the context and two responses, select the most important cognitive abilities and analyze critical issues.
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**Context:**
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{context}
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**Responses:**
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[The Begin of Response 1]
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{response1}
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[The End of Response 1]
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[The Begin of Response 2]
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{response2}
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[The End of Response 2]
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**Output Format:**
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[Quality Assessment Focus]
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Choose 1-3 abilities: Information Accuracy, Computational Precision, Logical Reasoning, Implementation Capability, Safety Awareness, Response Completeness, Instruction Adherence, Communication Clarity.
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[End of Quality Assessment Focus]
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[Quality Analysis for Response 1]
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- Critical Issues: [List specific issues or "None identified"]
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[End of Quality Analysis for Response 1]
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[Quality Analysis for Response 2]
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- Critical Issues: [List specific issues or "None identified"]
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[End of Quality Analysis for Response 2]"""
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# Generate Turn 1
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messages = [{"role": "user", "content": turn1_prompt}]
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input_ids = tokenizer.apply_chat_template(
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messages,
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return_tensors="pt",
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add_generation_prompt=True
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).to(model.device)
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outputs = model.generate(
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input_ids,
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max_new_tokens=8192,
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temperature=1.0,
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top_p=0.95,
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top_k=20,
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do_sample=True,
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pad_token_id=tokenizer.eos_token_id
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)
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turn1_response = tokenizer.decode(outputs[0][input_ids.shape[1]:], skip_special_tokens=False)
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# Format Turn 2: Branch-Conditioned Rethinking
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turn2_prompt = f"""You are making final comparative judgments using established evaluation priorities.
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**Evaluation Hierarchies:**
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- **Accuracy-Critical**: Correctness > Process > Presentation
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- **Creative/Open-Ended**: User Intent > Content Quality > Creativity
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- **Instruction-Following**: Adherence > Content > Clarity
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[The Begin of Analysis on Response 1]
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[Apply appropriate evaluation hierarchy]
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[The End of Analysis on Response 1]
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[The Begin of Analysis on Response 2]
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[Apply appropriate evaluation hierarchy]
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[The End of Analysis on Response 2]
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[The Begin of Ranking Score]
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\\boxed{{1 or 2}}
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[The End of Ranking Score]"""
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# Generate Turn 2
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messages.append({"role": "assistant", "content": turn1_response})
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messages.append({"role": "user", "content": turn2_prompt})
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input_ids = tokenizer.apply_chat_template(
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messages,
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return_tensors="pt",
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add_generation_prompt=True
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).to(model.device)
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outputs = model.generate(
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input_ids,
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max_new_tokens=8192,
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temperature=1.0,
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top_p=0.95,
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top_k=20,
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do_sample=True,
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pad_token_id=tokenizer.eos_token_id
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)
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final_response = tokenizer.decode(outputs[0][input_ids.shape[1]:], skip_special_tokens=False)
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```
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## Ethical Considerations:
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NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
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For more detailed information on ethical considerations for this model, please see the Model Card++ [Explainability](explainability.md), [Bias](bias.md), [Safety and Security](safety.md), and [Privacy](privacy.md) Subcards.
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Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
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## Citation
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If you find this model useful, please cite the following work:
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```bibtex
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@misc{jiao2025thinktwicebranchandrethinkreasoning,
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title={Think Twice: Branch-and-Rethink Reasoning Reward Model},
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author={Yizhu Jiao and Jiaqi Zeng and Julien Veron Vialard and Oleksii Kuchaiev and Jiawei Han and Olivier Delalleau},
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year={2025},
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eprint={2510.23596},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2510.23596},
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}
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```
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added_tokens.json
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{
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"</think>": 151668,
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"</tool_call>": 151658,
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"</tool_response>": 151666,
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"<think>": 151667,
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"<tool_call>": 151657,
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"<tool_response>": 151665,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|file_sep|>": 151664,
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"<|fim_middle|>": 151660,
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"<|fim_pad|>": 151662,
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"<|fim_prefix|>": 151659,
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"<|fim_suffix|>": 151661,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|repo_name|>": 151663,
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is string %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in content %}
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{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
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{%- endif %}
|
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{%- endif %}
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{%- if loop.index0 > ns.last_query_index %}
|
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{%- if loop.last or (not loop.last and reasoning_content) %}
|
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
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{%- else %}
|
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{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- if message.tool_calls %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if (loop.first and content) or (not loop.first) %}
|
||||
{{- '\n' }}
|
||||
{%- endif %}
|
||||
{%- if tool_call.function %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{%- if tool_call.arguments is string %}
|
||||
{{- tool_call.arguments }}
|
||||
{%- else %}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{%- endif %}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
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{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
|
||||
{%- if enable_thinking is defined and enable_thinking is false %}
|
||||
{{- '<think>\n\n</think>\n\n' }}
|
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{%- endif %}
|
||||
{%- endif %}
|
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30
config.json
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config.json
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{
|
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"architectures": [
|
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"Qwen3ForCausalLM"
|
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],
|
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"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"eos_token_id": 151645,
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 4096,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 12288,
|
||||
"max_position_embeddings": 40960,
|
||||
"max_window_layers": 36,
|
||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 36,
|
||||
"num_key_value_heads": 8,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000000,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.52.4",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
|
||||
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merges.txt
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merges.txt
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Load Diff
3
pytorch_model.bin
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3
pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
|
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oid sha256:454105a17932943679336e4bc0c80d9e5ab346f20b06859ecc45b98b532ab795
|
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size 32763075147
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31
special_tokens_map.json
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31
special_tokens_map.json
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|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
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"<|box_end|>",
|
||||
"<|quad_start|>",
|
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"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
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"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
239
tokenizer_config.json
Normal file
239
tokenizer_config.json
Normal file
@@ -0,0 +1,239 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151666": {
|
||||
"content": "</tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151667": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151668": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user