RewardHub

RewardHub is an end-to-end library for annotating data using state-of-the-art (SoTA) reward models, critic functions, and related processes. It is designed to facilitate the generation of preference training data or define acceptance criteria for agentic or inference scaling systems such as Best-of-N sampling or Beam-Search.

Getting Started

Installation

Basic Installation

For all functionality including HuggingFace, VLLM, and OpenAI backends:

git clone https://github.com/Red-Hat-AI-Innovation-Team/reward_hub.git
cd reward_hub
pip install -e .

PRM Installation (Qwen-PRM Support)

If you need to use Qwen Process Reward Models (e.g., Qwen/Qwen2.5-Math-PRM-7B), install with the prm extra:

pip install -e .[prm]

Development Installation

For development with additional tools (pytest, ruff, pre-commit):

pip install -e .[dev]

Usage Examples

RewardHub supports multiple types of reward models and serving methods. Here are the main ways to use the library:

Process Reward Models (PRM)

PRMs evaluate responses by analyzing the reasoning process:

from reward_hub import AutoRM

# Load a math-focused PRM using HuggingFace backend
model = AutoRM.load("Qwen/Qwen2.5-Math-PRM-7B", load_method="hf")

# Example conversation
messages = [
    [
        {"role": "user", "content": "What is 2+2?"},
        {"role": "assistant", "content": "Let me solve this step by step:\n1) 2 + 2 = 4\nTherefore, 4"}
    ]
]

# Get scores with full PRM results
results = model.score(messages, return_full_prm_result=True)
# Or just get the scores
scores = model.score(messages, return_full_prm_result=False)

Outcome Reward Models (ORM)

ORMs focus on evaluating the final response quality:

from reward_hub import AutoRM

# Load an ORM using HuggingFace backend
model = AutoRM.load("internlm/internlm2-7b-reward", load_method="hf")

scores = model.score([
    [
        {"role": "user", "content": "What is 2+2?"},
        {"role": "assistant", "content": "The answer is 4."}
    ]
])

DrSow Reward Model

DrSow uses density ratios between strong and weak models to evaluate responses:

Launch the strong and weak models first.

bash scripts/launch_drsow.sh Qwen/Qwen2.5-32B-instruct Qwen/Qwen2.5-32B

Then, you can launch client reward servers to acces the DrSow reward model.

from reward_hub import AutoRM
from reward_hub.drsow import DrSowConfig

drsow_config = DrSowConfig(
    strong_model_name="Qwen/Qwen2.5-32B-instruct",
    strong_port=8305,
    weak_model_name="Qwen/Qwen2.5-32B",
    weak_port=8306
)

model = AutoRM.load("drsow", load_method="openai", drsow_config=drsow_config)

# Get scores for responses
scores = model.score([
    [
        {"role": "user", "content": "What is 2+2?"},
        {"role": "assistant", "content": "The answer is 4."}
    ]
])

LLM-as-a-Judge

Use LLMs to evaluate conversation quality with customizable criteria:

from reward_hub.llm_judge import create_pointwise_judge, create_groupwise_judge, CriterionRegistry
from reward_hub.llm_judge.prompts import Criterion

# Pointwise: Score individual conversations (0-10)
judge = create_pointwise_judge(model="gpt-4o-mini", criterion="overall_quality", api_key="sk-...")
score = judge.score([{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}])

# Groupwise: Rank and select top N responses
judge = create_groupwise_judge(model="gpt-4o-mini", criterion="multi_step_tool_judge", api_key="sk-...")
scores = judge.score(conversations, top_n=2)  # Returns [1.0, 0.0, 1.0] for top-2

# Custom criteria
CriterionRegistry.register(Criterion(
    name="code_quality",
    content="Evaluate: readability, best practices, error handling, efficiency"
))
judge = create_pointwise_judge(model="gpt-4o-mini", criterion="code_quality", api_key="sk-...")

Built-in criteria: overall_quality, multi_step_tool_judge Supported providers: OpenAI, Anthropic, Google, Azure (via LiteLLM)

Supported Backends

RewardHub supports multiple serving backends:

• HuggingFace (load_method="hf"): Direct local model loading
• VLLM (load_method="vllm"): Optimized local serving
• OpenAI API (load_method="openai"): Remote API access

Supported Models

We support various reward models including:

Research

RewardHub serves as the official implementation of the paper:
Dr. SoW: Density Ratio of Strong-over-weak LLMs for Reducing the Cost of Human Annotation in Preference Tuning

The paper introduces CDR, a novel approach to generating high-quality preference annotations using density ratios tailored to domain-specific needs.