# PrefMMT: Modeling Human Preferences in Preference-based Reinforcement Learning with Multimodal Transformers

arXiv: https://arxiv.org/abs/2409.13683 · HTML: https://mlsanity.com/paper/2409.13683/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL training of language and agent models, one of 15 directions in Deep reinforcement learning methods.
- First posted on arXiv 20 Sep 2024.

## Placed in

- Deep reinforcement learning methods / [RL training of language and agent models](https://mlsanity.com/deeprl/rl-training-of-language-and-agent-models/)

## Built on by (most cited first, 2 of 2)

- [PrefMoE: Robust Preference Modeling with Mixture-of-Experts Reward Learning](https://mlsanity.com/paper/2605.00384/) (2026)
- [PAWS: Preference Learning with Advantage-Weighted Segments](https://mlsanity.com/paper/2606.11982/) (2026)

## Abstract (arXiv)

Preference-based reinforcement learning (PbRL) shows promise in aligning robot behaviors with human preferences, but its success depends heavily on the accurate modeling of human preferences through reward models. Most methods adopt Markovian assumptions for preference modeling (PM), which overlook the temporal dependencies within robot behavior trajectories that impact human evaluations. While recent works have utilized sequence modeling to mitigate this by learning sequential non-Markovian rewards, they ignore the multimodal nature of robot trajectories, which consist of elements from two distinctive modalities: state and action. As a result, they often struggle to capture the complex interplay between these modalities that significantly shapes human preferences. In this paper, we propose a multimodal sequence modeling approach for PM by disentangling state and action modalities. We introduce a multimodal transformer network, named PrefMMT, which hierarchically leverages intra-modal temporal dependencies and inter-modal state-action interactions to capture complex preference patterns. We demonstrate that PrefMMT consistently outperforms state-of-the-art PM baselines on locomotion tasks from the D4RL benchmark and manipulation tasks from the Meta-World benchmark.