Agentic Reasoning with Reinforcement Learning

Explore how reinforcement fine-tuning (RFT) enhances agentic reasoning capabilities in large language models through this 15-minute conference presentation. Discover the critical importance of explicit reasoning for achieving reliability, transparency, and generalization across complex computational tasks. Examine a detailed experimental case study using the Wordle game as a practical testbed, demonstrating how RFT successfully guides language models to develop structured reasoning strategies instead of relying on superficial pattern matching approaches. Learn about the broader landscape of reinforcement learning applications in reasoning systems through a comprehensive survey of related methodologies and techniques. Gain insights into current open research directions focused on improving robustness and efficiency in agentic systems, and understand how these advances contribute to more reliable and interpretable AI reasoning capabilities.