Summary of Scaling Laws For Reward Model Overoptimization in Direct Alignment Algorithms, by Rafael Rafailov et al.
Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms
by Rafael Rafailov, Yaswanth Chittepu, Ryan Park, Harshit Sikchi, Joey Hejna, Bradley Knox, Chelsea Finn, Scott Niekum
First submitted to arxiv on: 5 Jun 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 presents an investigation into the limitations of Direct Alignment Algorithms (DDAs) in Reinforcement Learning from Human Feedback (RLHF), a crucial component in Large Language Model (LLM) development. Although DAAs bypass the reward modeling phase, they still exhibit similar degradation patterns to classical RLHF methods, including over-optimization and reward hacking. The study formalizes and explores these issues across various objectives, training regimes, and model scales. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks into why some language models trained with human feedback don’t always get better over time. Researchers found that even when they skip the part where a reward model is created, these models can still have problems optimizing their performance. The study shows that this “reward hacking” issue happens in both old and new ways of training language models. |
Keywords
» Artificial intelligence » Alignment » Large language model » Optimization » Reinforcement learning from human feedback » Rlhf
