Summary of Quantile Regression For Distributional Reward Models in Rlhf, by Nicolai Dorka
Quantile Regression for Distributional Reward Models in RLHF
by Nicolai Dorka
First submitted to arxiv on: 16 Sep 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 Quantile Reward Models (QRMs), a novel approach to reinforcement learning from human feedback (RLHF) that learns a distribution over rewards instead of a single scalar value. QRMs use quantile regression to estimate a full, potentially multimodal distribution over preferences, capturing the diversity and complexity of human values. The method addresses label noise, conflicting preferences, and is shown to outperform traditional point-estimate models on RewardBench. Additionally, the paper demonstrates how the distributional estimates can be used in downstream applications, such as risk-aware reinforcement learning, resulting in LLM policies that generate fewer extremely negative responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning from human feedback (RLHF) helps big language models understand what humans like or dislike. Traditionally, RLHF uses simple rewards that don’t capture the complexity of human preferences. This paper introduces a new way to do reward modeling called Quantile Reward Models (QRMs). QRMs learn a full range of possible rewards instead of just one, which helps capture diverse human values and preferences. The method is better at handling noisy or conflicting feedback, and performs well on a benchmark test. It also shows how this approach can be used in other applications to make language models behave more positively. |
Keywords
» Artificial intelligence » Regression » Reinforcement learning » Reinforcement learning from human feedback » Rlhf
