Position: The Complexity of Perfect AI Alignment -- Formalizing the RLHF Trilemma

A new framework has been introduced to enforce hard linear constraints in deep learning models, addressing the need for compliance with physical laws and safety limits in safety-critical applications. This model-agnostic approach combines a task network focused on prediction accuracy with a safe network utilizing decision rules from stochastic and robust optimization, ensuring feasibility across the input space.

A novel architecture called Sparse Autoencoder-enhanced Reward Model (SARM) has been introduced to improve the interpretability of reward models used in Reinforcement Learning from Human Feedback (RLHF). This model integrates a pretrained Sparse Autoencoder into traditional reward models, aiming to provide clearer insights into how human preferences are mapped to LLM behaviors.

A comprehensive survey on Multimodal Generative Models (MGMs) has been published, detailing their evolution from text generation to various output modalities such as images, music, and video. The study categorizes six primary generative modalities and discusses foundational techniques like Self-Supervised Learning and Chain-of-Thought prompting that enable cross-modal capabilities.

A new framework called Multi-Value Alignment (MVA) has been proposed to address the challenges of aligning large language models (LLMs) with multiple human values, particularly when these values conflict. This framework aims to improve the stability and efficiency of multi-value optimization, overcoming limitations seen in existing methods like Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO).