This presentation examines how reinforcement fine-tuning (RFT) can be applied to enhance agentic reasoning in large language models. We begin by motivating the need for explicit reasoning, highlighting its role in reliability, transparency, and generalization across complex tasks. Building on this foundation, we showcase an experimental case study using the Wordle game as a testbed, demonstrating how RFT guides models to develop structured reasoning strategies rather than relying on shallow pattern matching. Finally, we present a concise survey of related approaches, situating RFT within the broader landscape of reinforcement learning for reasoning, and outlining open directions for improving robustness and efficiency in agentic systems.