Summary of Does Rlhf Scale? Exploring the Impacts From Data, Model, and Method, by Zhenyu Hou et al.
Does RLHF Scale? Exploring the Impacts From Data, Model, and Method
by Zhenyu Hou, Pengfan Du, Yilin Niu, Zhengxiao Du, Aohan Zeng, Xiao Liu, Minlie Huang, Hongning Wang, Jie Tang, Yuxiao Dong
First submitted to arxiv on: 8 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study investigates the scalability of Reinforcement Learning from Human Feedback (RLHF) in Large Language Models (LLMs). The authors systematically analyze key components in the RLHF framework, including model size, data composition, and inference budget, to understand their impact on performance. They find that increasing data diversity and volume improves reward model performance, allowing process-supervision models to scale better. For policy training, more response samples per prompt initially boost performance but quickly plateau. Larger reward models offer modest gains in policy training. In contrast, larger policy models benefit less from RLHF with a fixed reward model. The study concludes that RLHF scales less efficiently than pretraining, with diminishing returns from additional computational resources. To optimize RLHF performance within computational limits, the authors propose strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well Reinforcement Learning from Human Feedback (RLHF) works in Large Language Models (LLMs). The team studies what makes RLHF successful or not. They find that having more diverse and abundant data helps make the model better. For some tasks, having more responses to prompts can help initially but eventually stops improving performance. The study shows that RLHF doesn’t get much better with even more powerful computers. To make RLHF work best within the limits of available computing power, the researchers suggest some strategies. |
Keywords
» Artificial intelligence » Inference » Pretraining » Prompt » Reinforcement learning from human feedback » Rlhf
