LLM Misalignment via Adversarial RLHF Platforms

Reinforcement learning has shown remarkable performance in aligning language models with human preferences, leading to the rise of attention towards developing RLHF platforms. These platforms enable users to fine-tune models without requiring any expertise in developing complex machine learning algorithms. While these platforms offer useful features such as reward modeling and RLHF fine-tuning, their security and reliability remain largely unexplored. Given the growing adoption of RLHF and open-source RLHF frameworks, we investigate the trustworthiness of these systems and their potential impact on behavior of LLMs. In this paper, we present an attack targeting publicly available RLHF tools. In our proposed attack, an adversarial RLHF platform corrupts the LLM alignment process by selectively manipulating data samples in the preference dataset. In this scenario, when a user's task aligns with the attacker's objective, the platform manipulates a subset of the preference dataset that contains samples related to the attacker's target. This manipulation results in a corrupted reward model, which ultimately leads to the misalignment of the language model. Our results demonstrate that such an attack can effectively steer LLMs toward undesirable behaviors within the targeted domains. Our work highlights the critical need to explore the vulnerabilities of RLHF platforms and their potential to cause misalignment in LLMs during the RLHF fine-tuning process.

AI-Driven Innovations in Volumetric Video Streaming: A Review

Recent efforts to enhance immersive and interactive user experiences have driven the development of volumetric video, a form of 3D content that enables 6 DoF. Unlike traditional 2D content, volumetric content can be represented in various ways, such as point clouds, meshes, or neural representations. However, due to its complex structure and large amounts of data size, deploying this new form of 3D data presents significant challenges in transmission and rendering. These challenges have hindered the widespread adoption of volumetric video in daily applications. In recent years, researchers have proposed various AI-driven techniques to address these challenges and improve the efficiency and quality of volumetric content streaming. This paper provides a comprehensive overview of recent advances in AI-driven approaches to facilitate volumetric content streaming. Through this review, we aim to offer insights into the current state-of-the-art and suggest potential future directions for advancing the deployment of volumetric video streaming in real-world applications.

A Safe Exploration Strategy for Model-free Task Adaptation in Safety-constrained Grid Environments

Training a model-free reinforcement learning agent requires allowing the agent to sufficiently explore the environment to search for an optimal policy. In safety-constrained environments, utilizing unsupervised exploration or a non-optimal policy may lead the agent to undesirable states, resulting in outcomes that are potentially costly or hazardous for both the agent and the environment. In this paper, we introduce a new exploration framework for navigating the grid environments that enables model-free agents to interact with the environment while adhering to safety constraints. Our framework includes a pre-training phase, during which the agent learns to identify potentially unsafe states based on both observable features and specified safety constraints in the environment. Subsequently, a binary classification model is trained to predict those unsafe states in new environments that exhibit similar dynamics. This trained classifier empowers model-free agents to determine situations in which employing random exploration or a suboptimal policy may pose safety risks, in which case our framework prompts the agent to follow a predefined safe policy to mitigate the potential for hazardous consequences. We evaluated our framework on three randomly generated grid environments and demonstrated how model-free agents can safely adapt to new tasks and learn optimal policies for new environments. Our results indicate that by defining an appropriate safe policy and utilizing a well-trained model to detect unsafe states, our framework enables a model-free agent to adapt to new tasks and environments with significantly fewer safety violations.