Summary of A Theoretical Framework For Partially Observed Reward-states in Rlhf, by Chinmaya Kausik et al.
A Theoretical Framework for Partially Observed Reward-States in RLHF
by Chinmaya Kausik, Mirco Mutti, Aldo Pacchiano, Ambuj Tewari
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 In this paper, researchers investigate the theoretical foundations of reinforcement learning from human feedback (RLHF), a growing area in AI research. The current models of RLHF lack critical components, such as internal states and intermediate feedback, which can significantly impact the learning process. To address these limitations, the authors propose a new model called PORRL (reinforcement learning with partially observed reward-states). They demonstrate that this model subsumes various RL problems, including traditional RL and RLHF. The researchers then present two model-based methods for cardinal feedback, providing regret and sample complexity guarantees. Additionally, they discuss the benefits of model-free approaches like GOLF in settings with recursive internal states and dense intermediate feedback. The authors also explore dueling feedback, showing that a naive reduction to cardinal feedback is insufficient. Instead, they propose an explicit reduction that converts guarantees for cardinal regret to dueling regret. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how people help artificial intelligence learn by giving it feedback. Right now, the way AI learns from this feedback doesn’t take into account things like what’s going on inside a person’s head or if they’re getting helpful hints during an interaction. The researchers came up with a new model called PORRL to make up for these missing pieces. They show that this model can help AI learn faster and better align with human goals. They also explored different types of feedback, like when people are helping or competing against each other. |
Keywords
* Artificial intelligence * Reinforcement learning * Reinforcement learning from human feedback * Rlhf
