How to Evaluate Reward Models for RLHF
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arXiv
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| Format: | Preprint |
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2024
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| author | Frick, Evan Li, Tianle Chen, Connor Chiang, Wei-Lin Angelopoulos, Anastasios N. Jiao, Jiantao Zhu, Banghua Gonzalez, Joseph E. Stoica, Ion |
| author_facet | Frick, Evan Li, Tianle Chen, Connor Chiang, Wei-Lin Angelopoulos, Anastasios N. Jiao, Jiantao Zhu, Banghua Gonzalez, Joseph E. Stoica, Ion |
| contents | We introduce a new benchmark for reward models that quantifies their ability to produce strong language models through RLHF (Reinforcement Learning from Human Feedback). The gold-standard approach is to run a full RLHF training pipeline and directly probe downstream LLM performance. However, this process is prohibitively expensive. To address this, we build a predictive model of downstream LLM performance by evaluating the reward model on proxy tasks. These proxy tasks consist of a large-scale human preference and a verifiable correctness preference dataset, in which we measure 12 metrics across 12 domains. To investigate which reward model metrics are most correlated to gold-standard RLHF outcomes, we launch an end-to-end RLHF experiment on a large-scale crowdsourced human preference platform to view real reward model downstream performance as ground truth. Ultimately, we compile our data and findings into Preference Proxy Evaluations (PPE), the first reward model benchmark explicitly linked to post-RLHF real-world human preference performance, which we open-source for public use and further development. Our code and evaluations can be found at https://github.com/lmarena/PPE . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14872 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | How to Evaluate Reward Models for RLHF Frick, Evan Li, Tianle Chen, Connor Chiang, Wei-Lin Angelopoulos, Anastasios N. Jiao, Jiantao Zhu, Banghua Gonzalez, Joseph E. Stoica, Ion Machine Learning Artificial Intelligence Computation and Language We introduce a new benchmark for reward models that quantifies their ability to produce strong language models through RLHF (Reinforcement Learning from Human Feedback). The gold-standard approach is to run a full RLHF training pipeline and directly probe downstream LLM performance. However, this process is prohibitively expensive. To address this, we build a predictive model of downstream LLM performance by evaluating the reward model on proxy tasks. These proxy tasks consist of a large-scale human preference and a verifiable correctness preference dataset, in which we measure 12 metrics across 12 domains. To investigate which reward model metrics are most correlated to gold-standard RLHF outcomes, we launch an end-to-end RLHF experiment on a large-scale crowdsourced human preference platform to view real reward model downstream performance as ground truth. Ultimately, we compile our data and findings into Preference Proxy Evaluations (PPE), the first reward model benchmark explicitly linked to post-RLHF real-world human preference performance, which we open-source for public use and further development. Our code and evaluations can be found at https://github.com/lmarena/PPE . |
| title | How to Evaluate Reward Models for RLHF |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2410.14872 |