Large Language Models (LLMs) have demonstrated a powerful ability for text generation.However, achieving optimal results with a given prompt or instruction can be challenging, especially for billion-sized models.Additionally, undesired behaviors such as toxicity or hallucinations can manifest.While much larger models (e.g., ChatGPT) may demonstrate strength in mitigating these issues, there is still no guarantee of complete prevention.In this work, we propose formalizing text generation as a future-constrained generation problem to minimize undesirable behaviors and enforce faithfulness to instructions.The estimation of future constraint satisfaction, accomplished using LLMs, guides the text generation process.Our extensive experiments demonstrate the effectiveness of the proposed approach across three distinct text generation tasks: keywordconstrained generation (Lin et al., 2020), toxicity reduction (Gehman et al., 2020), and factual correctness in question-answering (Gao et al., 2023). 1

This paper investigates the user pairing in a full-duplex (FD) communication system, aiming at maximizing the overall data rate of the system by reducing inter-user interference. The traditional user pairing methods usually suffer from high computational complexity and therefore are not suitable for practical implementation. Inspired by the recent innovation of deep reinforcement learning (DRL), we develop a low-complexity algorithm for the user pairing problem in FD networks. We first transform the problem into a Markov decision process (MDP) to facilitate the implementation of DRL. We then utilize the semi-supervised training paradigm to speed up the training process by adding some expert experience to the replay buffer. Finally, our proposal is extensively tested via numerical simulation, which demonstrates that the DRL-based user pairing algorithm can achieve a good performance with a significantly reduced computational complexity.