Can Large Language Models Really Judge with Reasoning?
Introduction
Recent advancements in large language models (LLMs) have sparked interest in their reasoning and judgment capabilities. Researchers from Microsoft and Tsinghua University have developed Reward Reasoning Models (RRMs) to improve the alignment of LLMs by dynamically adjusting computational resources during evaluations.
The Role of Reinforcement Learning in LLMs
Reinforcement learning (RL) is crucial for refining LLMs after initial training. This process can utilize either human feedback (RLHF) or verifiable rewards (RLVR). While RLVR shows promise in mathematical reasoning, its effectiveness is limited by the need for training queries with clear, verifiable answers, making it less applicable to general queries.
Challenges with Current Reward Models
Current reward models fall into two categories: scalar and generative. Scalar models provide numeric scores for query-response pairs, while generative models offer feedback in natural language. However, both types often apply uniform computational resources across all inputs, which can lead to inefficiencies, especially for more complex queries.
Introducing Reward Reasoning Models (RRMs)
RRMs aim to overcome these limitations by incorporating explicit reasoning before assigning rewards. This reasoning phase enables adaptive allocation of computational resources for evaluating responses to complex tasks, resulting in improved reward modeling and support for varied evaluation scenarios.
Technical Specifications and Business Applications
RRMs utilize the Qwen2 model with a Transformer-decoder architecture, treating reward modeling as a text completion task. They generate reasoning processes followed by final judgments in an autoregressive manner. Each input consists of a query and two responses, with a clear preference determined without ties.
The RewardBench repository facilitates systematic analysis across multiple evaluation criteria, including instruction fidelity, helpfulness, accuracy, harmlessness, and detail level. RRMs enhance multi-response evaluation through ELO rating systems and knockout tournaments, optimizing the use of computational resources during testing.
Performance Evaluation
Evaluation results show that RRMs perform competitively against established benchmarks like RewardBench and PandaLM Test. The RRM-32B model achieves an impressive accuracy of 98.6% in reasoning tasks. Comparisons with DirectJudge models highlight the significant advantages of RRMs in utilizing computational resources effectively for complex queries.
In scenarios such as reward-guided best-of-N inference, RRMs outperform all baseline models without needing extra computational resources. Additionally, majority voting methods further improve outcomes across evaluated subsets. Post-training experiments indicate consistent enhancements in downstream performance on tasks like MMLU-Pro and GPQA.
Conclusion
The introduction of RRMs is a significant advancement in reward modeling for LLMs. By implementing explicit reasoning prior to reward assignment, RRMs effectively address the computational limitations of existing models. This innovative approach paves the way for developing complex reasoning capabilities without relying on explicit reasoning traces as supervision. The adaptability of RRMs in practical applications underscores their potential as a strong alternative to traditional scalar reward models.
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