Industry Talk: Financial Reasoning Agents: In-Context Reinforcement Learning and Test-Time Compute

Join Nicole Königstein and explore test-time compute efficiency.

13:00 EDT / 18:00 BST / 19:00 CET

Abstract:

Large Language Models (LLMs) are increasingly being integrated with reinforcement learning (RL) to push the boundaries of generalist AI agents. In finance, where real-time decision-making is critical, test-time compute efficiency plays a pivotal role in ensuring models can adapt dynamically to evolving market conditions. In-context reinforcement learning (ICRL) is emerging as a transformative approach, enabling LLMs to learn and refine on the fly without explicit fine-tuning. ICRL enhances adaptability in trading, risk assessment, and portfolio optimization. This paradigm shift moves us closer to AI agents capable of robust decision-making, paving the way for more autonomous and generalizable systems in high-stakes applications.

About the Speaker:

Nicole is the Co-Founder, CEO, and Co-Chief AI Officer at Quantmate, a deep-tech fintech company developing AI agents for portfolio management and strategy development via natural language. She is a globally recognized thought leader in large language models (LLMs) and agentic architectures, with a particular focus on their transformative applications in quantitative finance.

As a guest lecturer, Nicole shares her expertise in Python, machine learning, and deep learning at universities. She is also a frequent speaker at AI and quantitative finance events. Nicole has authored Math for Machine Learning and Transformers in Action with Manning Publications. Her forthcoming book, Transformers: The Definitive Guide – Applications Beyond NLP, will be published by O’Reilly Media