# Weak Human Preference Supervision For Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2007.12904 · HTML: https://mlsanity.com/paper/2007.12904/

## Key facts

- Cited by 6 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.
- First posted on arXiv 25 Jul 2020.

## Placed in

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

## Built on by (most cited first, 6 of 6)

- [PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training](https://mlsanity.com/paper/2106.05091/) (2021)
- [Skill Preferences: Learning to Extract and Execute Robotic Skills from Human Feedback](https://mlsanity.com/paper/2108.05382/) (2021)
- [PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling](https://mlsanity.com/paper/2404.13423/) (2024)
- [DIPPER: Direct Preference Optimization to Accelerate Primitive-Enabled Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2406.10892/) (2024)
- [Direct Preference Optimization for Primitive-Enabled Hierarchical RL: A Bilevel Approach](https://mlsanity.com/paper/2411.00361/) (2024)
- [Themis: An explainable AI-enabled framework for Reinforcement Learning with Human Feedback](https://mlsanity.com/paper/2606.24622/) (2026)

## Abstract (arXiv)

The current reward learning from human preferences could be used to resolve complex reinforcement learning (RL) tasks without access to a reward function by defining a single fixed preference between pairs of trajectory segments. However, the judgement of preferences between trajectories is not dynamic and still requires human input over thousands of iterations. In this study, we proposed a weak human preference supervision framework, for which we developed a human preference scaling model that naturally reflects the human perception of the degree of weak choices between trajectories and established a human-demonstration estimator via supervised learning to generate the predicted preferences for reducing the number of human inputs. The proposed weak human preference supervision framework can effectively solve complex RL tasks and achieve higher cumulative rewards in simulated robot locomotion -- MuJoCo games -- relative to the single fixed human preferences. Furthermore, our established human-demonstration estimator requires human feedback only for less than 0.01% of the agent's interactions with the environment and significantly reduces the cost of human inputs by up to 30% compared with the existing approaches. To present the flexibility of our approach, we released a video (https://youtu.be/jQPe1OILT0M) showing comparisons of the behaviours of agents trained on different types of human input. We believe that our naturally inspired human preferences with weakly supervised learning are beneficial for precise reward learning and can be applied to state-of-the-art RL systems, such as human-autonomy teaming systems.