Summary of How to Evaluate Reward Models For Rlhf, by Evan Frick et al.
How to Evaluate Reward Models for RLHF
by Evan Frick, Tianle Li, Connor Chen, Wei-Lin Chiang, Anastasios N. Angelopoulos, Jiantao Jiao, Banghua Zhu, Joseph E. Gonzalez, Ion Stoica
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a new benchmark for reward models that evaluates their ability to produce strong language models through Reinforcement Learning from Human Feedback (RLHF). The gold-standard approach is to run a full RLHF training pipeline and directly probe downstream Large Language Model (LLM) performance. However, this process is prohibitively expensive. To address this, the authors 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 dataset and a verifiable correctness preference dataset, in which they measure 12 metrics across 12 domains. The authors investigate which reward model metrics are most correlated to gold-standard RLHF outcomes by launching an end-to-end RLHF experiment on a large-scale crowdsourced human preference platform. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to test how well language models are developed using feedback from humans. It’s like a game where you try different ways to get good results and see what works best. The authors tested many different methods to see which ones work best for creating strong language models. They used two main types of tests: one that shows how people prefer certain answers, and another that checks if the answers are correct. By combining these test results with metrics from 12 different areas, they created a benchmark called Preference Proxy Evaluations (PPE) that can be used to develop better language models. |
Keywords
* Artificial intelligence * Large language model * Reinforcement learning from human feedback * Rlhf
