RewardBench (Evaluating Reward Models for Language Modeling)

What is RewardBench?

RewardBench is a benchmarking framework designed to evaluate the effectiveness and safety of reward models (RMs) used in language modeling. These models are crucial for aligning language models with human preferences, especially when employing Reinforcement Learning from Human Feedback (RLHF).

Leaderboard

The RewardBench leaderboard presents a concise overview of reward model performance. It ranks models based on their overall score, which combines results from chat scenarios, safety evaluations, and reasoning tasks.

The top-performing model is nvidia/Nemotron-4-340B-Reward, a custom classifier with a score of 92.2. Other high-ranking models include RLHFlow/ArmoRM-Llama3-8B-v0.1 and Cohere May 2024.

The leaderboard encompasses various model types, such as custom classifiers, generative models, and sequential classifiers, offering a factual comparison of their capabilities in language modeling and human preference alignment.

Updated June 28, 2024

RewardBench v1 source and protocol caveat

The page archived this RewardBench v1 table on June 28, 2024. It reports the v1 overall score alongside Chat, Chat Hard, Safety, and Reasoning category scores. The archived page did not identify the exact leaderboard revision URL or explain the asterisks, and it omitted ranks 12 and 13; those details are preserved rather than inferred. The RewardBench v1 paper documents the benchmark protocol, but this page's history does not prove that every row above came from one immutable paper table.

This table is retained for its v1 category breakdown, not as a current ranking. RewardBench 2 changes the dataset, domains, candidate-response format, and scoring procedure, so no v1 score above should be compared numerically with a v2 score below.

RewardBench 2 leaderboard snapshot

RewardBench 2 replaces the original benchmark's chat, chat-hard, safety, and reasoning aggregate with six domains: factuality, precise instruction following, math, safety, focus, and ties. Its overall score is the unweighted average across those six domains. Standard questions ask a model to identify one correct response among four completions, giving a 25% random baseline; the ties domain uses a separate score that also rewards calibrated treatment of equivalent correct answers.

The following top-five snapshot comes from the RewardBench 2 paper, last revised April 23, 2026. Check the official RewardBench leaderboard for later submissions. This date and source apply only to the v2 table, while the June 28, 2024 archival date applies only to the v1 table.

RewardBench v1 and RewardBench 2 use different datasets, domains, response formats, and scoring procedures. Their absolute scores are incompatible, so changes between a model's v1 and v2 results should not be interpreted as a direct performance gain or loss.

RewardBench 2 features

• Diverse Evaluation Domains — RewardBench 2 tests factuality, precise instruction following, math, safety, focus, and handling of ties between equally valid answers. Most prompts use a best-of-four format with one correct and three rejected completions.

• Downstream Analysis — The RewardBench 2 paper studies how benchmark performance relates to best-of-N inference and RLHF training. It also cautions that reward-model and policy-model lineage can materially affect downstream RLHF results.

• Installation and Usage — RewardBench supports local and API-based models. Its v2 scripts evaluate conventional reward models and generative judges, including ranking-based and rating-based judge prompts.

• Comprehensive Data and Code Availability — The RewardBench repository provides common inference, formatting, analysis, and RewardBench 2 evaluation code for reward models and generative judges.

RewardBench 2 evaluation process

RewardBench 2 uses accuracy-based tests to assess reward models and generative judges across multiple skills:

Evaluation details

• Prompt and completion construction — Most prompts are previously unused human queries. Each standard example pairs one correct completion with three rejected completions.
• Model scoring — Reward models score the candidate completions, while generative judges rank candidates or rate them individually. The evaluation checks whether the model identifies the correct response.
• Domain aggregation — Results are calculated for each of the six domains, then averaged without domain weighting. The ties metric also checks whether valid answers are treated as equivalent while still outranking incorrect answers.

RewardBench 2 interpretation

RewardBench 2 provides a challenging, multi-domain comparison for reward models and generative judges. A high aggregate score remains only one selection signal: downstream performance can depend on the policy model, training setup, target domain, and model lineage, so teams should validate candidate reward models in their intended workflow.

Key Features of RewardBench

• Diverse Evaluation Metrics — RewardBench assesses reward models across several categories such as chat, reasoning, and safety. It includes datasets like AlpacaEval, MT Bench, LLMBar, and various refusal and reasoning tests, ensuring a comprehensive evaluation of the models' performance in different scenarios.

• In-depth Analysis of Reward Models — The framework provides insights into various aspects of reward models, such as their propensity to over-optimize and deviate from the initial data distribution. It highlights the potential of reward hacking and evaluates strategies to mitigate these issues, including ensemble methods, weight averaging, and constrained optimization.

• Installation and Usage — RewardBench supports both local and API-based models, making it versatile for different experimental setups. Users can run generative reward models using simple commands, and there is support for contributing new models to the leaderboard through GitHub.

• Comprehensive Data and Code Availability — The dataset consists of prompt-win-lose trios and other structured queries to benchmark reward models. This dataset is available on platforms like Hugging Face and GitHub, providing researchers with the tools needed to evaluate and improve their reward models.

How does RewardBench work?

RewardBench employs a systematic evaluation process to assess the performance and safety of reward models:

Evaluation Process

• Training Data — Involves collecting human preference data for various prompts and completions.
• Model Training — Uses methods like Direct Preference Optimization (DPO) and other classifier-based approaches to predict human preferences.
• Performance Metrics — Includes win-rate comparisons against reference models, evaluations on multi-turn conversations, and assessments on specific tasks like math reasoning and safety-related refusals.

What is the purpose of RewardBench?

RewardBench serves as a standardized evaluation framework for reward models, crucial for aligning language models with human values and preferences. It provides a comprehensive assessment across diverse domains, identifies potential issues like over-optimization and reward hacking, evaluates mitigation strategies, and offers a benchmark for comparing different models and approaches. This robust framework enables researchers and developers to systematically improve the performance and safety of reward models in language modeling.

Future Directions for RewardBench Research

Future research directions for RewardBench focus on enhancing its capabilities and scope. Key areas include expanding task diversity to challenge reward models more comprehensively, refining evaluation metrics to better capture nuanced performance aspects and human preference alignment, and studying long-term effects of reward models on language model behavior. Additionally, improving cross-model comparison methodologies and integrating RewardBench with other evaluation frameworks will contribute to a more holistic assessment of language models, advancing the field's understanding of reward model efficacy and safety.

Conclusion

RewardBench represents a significant step forward in the evaluation and improvement of reward models for language modeling. By providing a standardized, comprehensive framework for assessing these models, it contributes to the ongoing efforts to align AI systems with human values and preferences. As the field of AI continues to evolve, tools like RewardBench will play a crucial role in ensuring the development of safe and effective language models.

For further details, you can visit the RewardBench GitHub repository and access the original paper on arXiv.