Summary of Zeroth-order Policy Gradient For Reinforcement Learning From Human Feedback Without Reward Inference, by Qining Zhang et al.
Zeroth-Order Policy Gradient for Reinforcement Learning from Human Feedback without Reward Inference
by Qining Zhang, Lei Ying
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 This paper tackles challenges in Reinforcement Learning from Human Feedback (RLHF) by developing two novel algorithms that eliminate the need for reward inference. The authors address fundamental issues like distribution shift, overfitting, and problem misspecification in RLHF pipelines. To achieve this, they propose estimating local value functions from human preferences and using zeroth-order gradient approximators to approximate policy gradients. The resulting methods demonstrate polynomial convergence rates and outperform popular baselines like Direct Preference Optimization (DPO) and Proximal Policy Optimization (PPO). This work highlights the potential for efficient RLHF solutions without reward inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about helping computers learn from people’s preferences without needing to understand what makes people happy. Right now, it’s tricky to teach computers using human feedback because the computer might not fully grasp what we want. The authors found a way to skip that step and directly optimize the computer’s behavior without understanding why it’s good or bad. They did this by estimating how much better one action is than another based on people’s preferences. This new approach worked well in simulations and was even better than other methods that tried to understand rewards. |
Keywords
» Artificial intelligence » Inference » Optimization » Overfitting » Reinforcement learning from human feedback » Rlhf
