

\section{Multimodal Reinforcement Learning}
Multimodal learning involves models that process and relate information from multiple data modalities (e.g. vision, text, speech), enabling more comprehensive understanding than single-modality systems. By combining modalities, models can make more robust predictions and capture complementary information that one modality alone might miss. Such multimodal approaches have benefits across various applications, such as image caption and image generation. Given these advantages, multimodal learning has become increasingly significant as AI systems aim to perceive and reason more like humans, who naturally integrate sight, sound, and language \citep{baltruvsaitis-etal:2018multimodal}.

In the era of LLM, we can extend their capabilities into the multimodal domain by training them with diverse data types. For example, we can develop a visual language model by training an LLM with image-text pairs using SFT. Here, we return to the issue of aligning models with human preferences. Ideally, once aligned with human preferences through RL, the LLM would also generalize the target modality well by the SFT. However, the reality does not always meet expectations. Although an LLM may align well with human preferences in one aspect, it often struggles to generalize this alignment across a different modality. Thus, to align multimodal models with human preferences, we perform RL to enhance their performance further within the specific modality.

In this section, we use the visual language, speech generation, and diffusion models to discuss the application of RL in multimodal models.


\subsection{Visual Language Models}
While RL is commonly used to train LLMs, its application to other domains has been a prominent research topic. In multimodal language models\footnote{A multimodal language model is defined as a model that integrates an LLM with a multimodal encoder, such as CLIP \citep{radford-etal:2021learning}, allowing the LLM to process inputs beyond text, such as images. Recent literature has also introduced the use of LLMs for generating outputs in non-text modalities, such as images and speech \citep{xu-etal:2025qwen2,zhang-etal:2024mm}. However, in this section, we focus on the former definition of multimodal language models.}, for example, a notable trend is to perform RL training to improve their trustworthiness and helpfulness. This section considers Visual Language Models (VLMs), which connect a visual encoder to an LLM through a linear projector, facilitating general-purpose visual and language understanding. VLMs are currently the most explored extension in multimodal language research, and they form the foundation for many open-source multimodal language models, such as Qwen2.5-VL \citep{qwenTeam:2025qwen2.5-VL} and LLaMA-3.2-11B-Vision \citep{grattafiori-etal:2024llama}.

Training LLMs and VLMs with RL exhibits only minimal differences, primarily related to the input content. Unlike the textual input used for LLMs, the input for VLMs typically comprises a combination of one or multiple images and an instruction, denoted by $(\mathrm{\mathbf{I}}, \mathrm{\mathbf{x}})$, where $\mathrm{\mathbf{I}}$ represents the input images. These images are encoded into representations that are either concatenated with instruction embeddings or integrated through cross-attention mechanisms into the LLM. In practice, this subtle difference does not significantly affect the applicability of RL algorithms to VLMs. As a result, the RL training process for LLMs, as described in Section \ref{sec:example-using-rl-training-llms}, can be seamlessly adapted to train VLMs without major improvements \citep{yu-etal:2024rlhf,wang-etal:2024rovrm,zang2025internlm,ji2025safe}.

\begin{figure*}[!t]
    \centering
    \includegraphics[width=0.98\textwidth]{section7/Figures/rovrm.pdf}
    \caption{An overview of the multi-stage training approach for visual reward models.}
    \label{fig:preference-transfer}
\end{figure*}

However, training VLMs with RL is not a low-hanging fruit in practical applications. This is because it typically encounters the challenge of training a visual reward model due to the scarcity of high-quality visual preference data. One straightforward approach to address this issue is to generate visual preference data through the automatic preference data generation method described in Section \ref{sec:automatic-preference-data-generation} \citep{yu-etal:2024rlaif}. Another alternative is a multi-stage training approach for the visual reward model, motivated by a simple idea: human preferences are well captured in text, and these preferences can be transferred across modalities \citep{wang-etal:2024rovrm}. By leveraging textual preference data, this approach reduces the dependence on visual preference data in training a visual reward model. More specifically, as illustrated in Figure \ref{fig:preference-transfer}, we can train a visual reward model in the following three stages:
\begin{itemize}
    \item Stage 1: pre-training with large-scale textual preference data. Given the transferability of human preferences across different modalities, we can begin by using large-scale textual preference data to pre-train the visual reward model. Note that the projector parameters are frozen without images. This stage can be considered as providing a stronger starting point for training the visual reward model, as it enables the model to pre-learn general human preferences.
    \item Stage 2: fine-tuning with image caption-based preference data. Pre-learned human preferences cannot be directly applied to vision tasks due to both \textit{task gap} and \textit{modality gap}. To bridge the task gap, we fine-tune the reward model using image caption-based preference data in this stage. The rationale behind this approach is that general textual preference data does not cover vision-specific tasks, such as ``Please describe the content in this image''. We use such data to fine-tune the model, adapting it to vision-specific tasks. The projector parameters are frozen at this stage.
    \item Stage 3: fine-tuning with small-scale visual preference data. To further bridge the modality gap, we use visual preference data in the final stage to train the model. Note that in this phase, we also train the projector parameters.
\end{itemize}

In this process, not all preference data may align with the preferences used in subsequent phases, potentially leading to preference conflicts. We can enhance the visual reward model through data selection techniques, such as LESS \citep{xia-etal:2024less} to address this. In fact, preference transfer is effective across modalities and has also been shown to work across different tasks and languages \citep{cheng-etal:2023everyone,wu-etal:2024reuse}. Interested readers can refer to these papers for more detailed discussions of these topics. 

As discussed in Section~\ref{sec:generative-reward-models}, reward reasoning models have demonstrated superior performance in reward prediction. A natural question then arises: \textit{can reward reasoning capabilities also be transferred from text to multimodal settings?} Recent work by \cite{wang2026msrl} provides empirical evidence supporting this hypothesis. By following a similar multi-stage training paradigm, they show that reward reasoning capabilities can indeed be effectively transferred across modalities, further enhancing the performance of visual reward models. 



Apart from preference data, another approach to improving the visual model is to integrate additional image content, such as image captions, into the reward model \citep{sun-etal:2023aligning}. This approach aims to achieve factually augmented reward prediction, addressing reward hacking. Specifically, in the original setup, the reward model predicts a reward based solely on the image, input, and output; that is, the reward model’s input is $[\mathbf{I}, \mathbf{x}, \mathbf{y}]$. In the factually augmented setup, the reward model also receives additional input in the form of the textual image caption $\mathbf{C}$, resulting in an input of $[\mathbf{I}, \mathbf{C}, \mathbf{x}, \mathbf{y}]$. The basic idea is that the backbone of the visual reward model remains a well-trained LLM with an in-context learning ability. With this ability, we can provide additional content to help the model predict rewards more accurately. 

While our discussion primarily focuses on models for visual inputs in the context of visual language models, the RL techniques are adaptable across inputs of various modalities, such as video and audio.





% visual language model as reward model


% 视觉奖励模型训练大致思路
% image reward
% 融合文本数据进行提升
% 视觉奖励模型应用场景---对齐图生文模型   文生图评估模型

% 视觉语言模型的定义

% 框架
% Rlhf-v: Towards trustworthy mllms via behavior alignment from fine-grained correctional human feedback
% Aligning Large Multimodal Models with Factually Augmented RLHF

% 偏好数据
% RLAIF-V
% Silkie: Preference Distillation for Large Visual Language Models
% RoVRM

\subsection{Speech Generation Models}
% SpeechAlign: Aligning Speech Generation to Human Preferences



\subsection{Diffusion Models}
% Image Reward
% 为什么要进行强化学习
% 大体训练思路和流程
% DDPO (denoising diffusion preference optimization model)

