# Crowd-PrefRL: Preference-Based Reward Learning from Crowds

arXiv: https://arxiv.org/abs/2401.10941 · HTML: https://mlsanity.com/paper/2401.10941/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Reward modeling and RLHF, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 20 of its 36 references that are in the map.
- First posted on arXiv 17 Jan 2024.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Reward modeling and RLHF](https://mlsanity.com/alignment/reward-modeling-and-rlhf/)

## Builds on (most cited first, 20 of 20)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [DeepMind Control Suite](https://mlsanity.com/paper/1801.00690/) (2018)
- [Deep Reinforcement Learning at the Edge of the Statistical Precipice](https://mlsanity.com/paper/2108.13264/) (2021)
- [Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2307.15217/) (2023)
- [Towards Measuring the Representation of Subjective Global Opinions in Language Models](https://mlsanity.com/paper/2306.16388/) (2023)
- [PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training](https://mlsanity.com/paper/2106.05091/) (2021)
- [Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations](https://mlsanity.com/paper/1904.06387/) (2019)
- [Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF](https://mlsanity.com/paper/2312.08358/) (2023)
- [B-Pref: Benchmarking Preference-Based Reinforcement Learning](https://mlsanity.com/paper/2111.03026/) (2021)
- [Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets](https://mlsanity.com/paper/1705.10479/) (2017)
- [Group Preference Optimization: Few-Shot Alignment of Large Language Models](https://mlsanity.com/paper/2310.11523/) (2023)
- [Imitation Learning by Estimating Expertise of Demonstrators](https://mlsanity.com/paper/2202.01288/) (2022)
- [Learning Reward Functions from Scale Feedback](https://mlsanity.com/paper/2110.00284/) (2021)
- [Ranking and combining multiple predictors without labeled data](https://mlsanity.com/paper/1303.3257/) (2013)
- [Breadcrumbs to the Goal: Goal-Conditioned Exploration from Human-in-the-Loop Feedback](https://mlsanity.com/paper/2307.11049/) (2023)
- [Batch Reinforcement Learning from Crowds](https://mlsanity.com/paper/2111.04279/) (2021)

## Built on by (most cited first, 7 of 7)

- [Corruption Robust Offline Reinforcement Learning with Human Feedback](https://mlsanity.com/paper/2402.06734/) (2024)
- [Learning From Crowdsourced Noisy Labels: A Signal Processing Perspective](https://mlsanity.com/paper/2407.06902/) (2024)
- [Scalable Interactive Machine Learning for Future Command and Control](https://mlsanity.com/paper/2402.06501/) (2024)
- [Human-Inspired Multi-Level Reinforcement Learning](https://mlsanity.com/paper/2501.07502/) (2025)
- [Active Query Selection for Crowd-Based Reinforcement Learning](https://mlsanity.com/paper/2508.19132/) (2025)
- [Implicit Safety Alignment from Crowd Preferences](https://mlsanity.com/paper/2605.21822/) (2026)
- [BA-DPO: Bias-Adjusted Direct Preference Optimization for Language Model Alignment](https://mlsanity.com/paper/2609.35044/) (2026)

## Abstract (arXiv)

Preference-based reinforcement learning (RL) provides a framework to train AI agents using human feedback through preferences over pairs of behaviors, enabling agents to learn desired behaviors when it is difficult to specify a numerical reward function. While this paradigm leverages human feedback, it typically treats the feedback as given by a single human user. However, different users may desire multiple AI behaviors and modes of interaction. Meanwhile, incorporating preference feedback from crowds (i.e. ensembles of users) in a robust manner remains a challenge, and the problem of training RL agents using feedback from multiple human users remains understudied. In this work, we introduce a conceptual framework, Crowd-PrefRL, that integrates preference-based RL approaches with techniques from unsupervised crowdsourcing to enable training of autonomous system behaviors from crowdsourced feedback. We show preliminary results suggesting that Crowd-PrefRL can learn reward functions and agent policies from preference feedback provided by crowds of unknown expertise and reliability. We also show that in most cases, agents trained with Crowd-PrefRL outperform agents trained with majority-vote preferences or preferences from any individual user, especially when the spread of user error rates among the crowd is large. Results further suggest that our method can identify the presence of minority viewpoints within the crowd in an unsupervised manner.