1. Introduction

Reward model evaluation is crucial for understanding how well your models can judge, rank, and score responses in various scenarios. RM-Gallery provides a comprehensive suite of evaluation benchmarks, each designed to test different aspects of reward model capabilities.

This overview will help you understand:

• What each benchmark measures - The specific capabilities and scenarios tested
• When to use each benchmark - Guidelines for selecting appropriate evaluation tools
• How benchmarks complement each other - Building a complete evaluation strategy
• Key metrics and interpretation - Understanding evaluation results

2. Available Benchmarks

2.1 RewardBench 2.0

Focus: Comprehensive multi-domain evaluation

RewardBench 2.0 is the most comprehensive benchmark, covering a wide range of scenarios including: - Chat interactions - Safety and harmlessness - Reasoning capabilities - Code generation - Mathematical problem-solving

Best for: General-purpose reward model evaluation and comparing models across diverse tasks.

Key Metrics: - Overall accuracy - Per-category performance - Domain-specific breakdowns

→ Learn more about RewardBench

2.2 JudgeBench

Focus: LLM judge evaluation with multiple protocols

JudgeBench evaluates the ability of reward models to act as judges in pairwise comparisons. It supports multiple judging protocols: - Vanilla judge - Arena-Hard style - AutoJ format - Prometheus2 evaluation - Skywork-Critic approach

Best for: Testing models specifically designed for comparative evaluation and judge applications.

Key Metrics: - Pairwise accuracy - Source-wise performance - Position bias analysis

→ Learn more about JudgeBench

2.3 RM-Bench

Focus: Style-aware evaluation

RM-Bench introduces a unique 3x3 matrix evaluation approach that tests how models handle different response formats: - Concise responses - Detailed plain text - Detailed markdown formatting

Best for: Evaluating models that need to handle diverse response styles and formats.

Key Metrics: - Hard/Normal/Easy accuracy tiers - Style preference patterns - Multi-domain coverage (chat, code, math, safety)

→ Learn more about RM-Bench

2.4 RMB (Reward Model Benchmark)

Focus: Real-world scenario coverage

RMB provides extensive coverage with 49+ real-world scenarios, evaluating models across: - Helpfulness: brainstorming, classification, code generation, math, reasoning - Harmlessness: safety, toxicity detection, harmful content avoidance

Best for: Comprehensive real-world performance assessment across diverse use cases.

Key Metrics: - Pairwise comparison accuracy - Category-wise breakdowns - Helpfulness vs. harmlessness balance

→ Learn more about RMB

2.5 Conflict Detector

Focus: Logical consistency analysis

The Conflict Detector identifies logical inconsistencies in model judgments: - Symmetry conflicts: Contradictory preferences (A > B and B > A) - Transitivity conflicts: Circular logic (A > B > C but C > A) - Cycle detection: Complex preference loops

Best for: Testing model reliability and consistency in decision-making.

Key Metrics: - Conflict rates by type - Consistency scores - Logical coherence analysis

→ Learn more about Conflict Detector

3. Choosing the Right Benchmark

Quick Decision Guide

Evaluation Strategy

For a thorough evaluation, we recommend a multi-stage approach:

1. Baseline Assessment (RewardBench 2.0)
2. Establish overall capabilities

3. Identify strength/weakness domains

4. Specialized Testing (Based on use case)

5. JudgeBench for judge applications
6. RM-Bench for style-aware tasks

7. RMB for specific scenario coverage

8. Consistency Validation (Conflict Detector)

9. Verify logical coherence
10. Test reliability at scale

4. Common Evaluation Workflow

Regardless of which benchmark you choose, the typical workflow follows these steps:

Step 1: Setup Environment

# Install dependencies
pip install rm-gallery

# Configure API keys if using API-based models
import os
os.environ["OPENAI_API_KEY"] = "your-key"

Step 2: Download Benchmark Data

# Each benchmark has its own dataset
mkdir -p data/benchmarks
cd data/benchmarks

# Download specific benchmark (example for RewardBench)
git clone https://github.com/benchmark-repo

Step 3: Configure Evaluation

from rm_gallery.gallery.evaluation import BenchmarkEvaluator

evaluator = BenchmarkEvaluator(
    model_name="your-model",
    benchmark="rewardbench2",
    config={
        "batch_size": 8,
        "max_workers": 4
    }
)

Step 4: Run Evaluation

results = evaluator.evaluate()

Step 5: Analyze Results

# View overall metrics
print(f"Overall Accuracy: {results['accuracy']}")

# Per-category breakdown
for category, score in results['categories'].items():
    print(f"{category}: {score}")

# Export detailed results
results.export("results/evaluation_report.json")

5. Understanding Metrics

Accuracy-Based Metrics

Most benchmarks report accuracy as the primary metric: - Percentage of correct preferences/rankings - Typically broken down by category/domain - Higher is better (range: 0-100%)

Ranking Metrics

Some benchmarks use ranking-based evaluation: - Spearman correlation: Rank order correlation - Kendall's tau: Pairwise agreement measure - Both range from -1 to 1 (higher is better)

Consistency Metrics

Conflict Detector provides unique consistency metrics: - Conflict rate: Percentage of logical inconsistencies - Coherence score: Overall logical consistency - Lower conflict rates indicate better reliability

6. Best Practices

Performance Optimization

• Use parallel processing for faster evaluation
• Set appropriate batch sizes based on your hardware
• Enable caching to avoid redundant API calls

Result Interpretation

• Don't rely on a single metric - examine category breakdowns
• Compare against baseline models for context
• Look for consistent patterns across multiple benchmarks

Iterative Improvement

1. Identify weaknesses from evaluation results
2. Refine your model (training data, rubrics, architecture)
3. Re-evaluate on the same benchmarks
4. Track progress over time

7. Benchmark Comparison Matrix

8. Next Steps

Ready to start evaluating? Choose your benchmark:

• RewardBench 2.0 - Start here for comprehensive evaluation
• JudgeBench - For judge/evaluator applications
• RM-Bench - For style-aware testing
• RMB - For extensive scenario coverage
• Conflict Detector - For consistency testing

Each benchmark page provides detailed setup instructions, code examples, and result interpretation guidelines.

Additional Resources

• Building RM Overview - Learn how to build reward models
• RM Library - Pre-built reward models
• Best Practices - Evaluation best practices