The field of conversational recommendation and reinforcement learning is moving towards more sophisticated and effective methods for capturing user preferences and optimizing recommendation systems. Recent developments have focused on improving the accuracy and robustness of conversational recommendation models, as well as enhancing the efficiency of reinforcement learning algorithms. Notably, researchers are exploring the use of large language models, uncertainty-aware methods, and hybrid approaches to address challenges such as reward hacking and data noise. These innovations have the potential to significantly improve the performance of recommendation systems and enable more effective human-computer interaction. Noteworthy papers include: Empowering Retrieval-based Conversational Recommendation with Contrasting User Preferences, which proposes a novel conversational recommender model that leverages contrasting user preferences to improve recommendation accuracy. Sharpe Ratio-Guided Active Learning for Preference Optimization in RLHF, which introduces an active learning approach to efficiently select prompt and preference pairs using a risk assessment strategy based on the Sharpe Ratio. LaViC: Adapting Large Vision-Language Models to Visually-Aware Conversational Recommendation, which integrates compact image representations into dialogue-based recommendation systems to capture visual attributes of products.