Recent advancements in large language models (LLMs) have been highlighted through various studies focusing on their decision-making capabilities and reinforcement learning techniques. A paper from Google DeepMind discusses how LLMs can learn optimal exploration strategies via algorithm distillation and inference-time support, addressing the challenge of making decisions under uncertainty. Additionally, the introduction of Reverse Curriculum Reinforcement Learning (R3) aims to enhance LLM reasoning without the need for extensive process annotations, tackling issues related to sparse rewards and high annotation costs. Researchers from Imperial College London have created a benchmark for multi-hop reasoning, revealing the complexities LLMs face in this area. The new benchmark, Preference Proxy Evaluations (PPE), evaluates reward models and their effectiveness in guiding reinforcement learning from human feedback (RLHF). It includes over 16,000 prompts and 32,000 diverse model responses, aiming to determine how well reward models can predict RLHF performance. Furthermore, a new method called in-context preference learning (ICPL) has demonstrated a 30-fold increase in query efficiency for LLMs in RLHF tasks. Collectively, these studies signify a substantial step forward in enhancing LLM capabilities in reasoning and decision-making tasks.