Adaptive Reasoning in LLMs: From Post-Training to Test-Time Learning (partially remote)

Large language models are increasingly deployed in settings that require extended, multi-step reasoning, where a single forward pass is often insufficient. In response, recent research has explored a range of mechanisms that adapt model behavior beyond pretraining, including post-training refinement, test-time training, and agentic inference strategies. While these approaches have shown promise, they are often studied in isolation, making it difficult to understand their shared structure, trade-offs, and failure modes.

This tutorial presents a unified view of these methods through the lens of adaptive reasoning control loops. We examine how LLMs iteratively generate reasoning traces, receive structured feedback, and adapt their behavior during inference, and how post-training, test-time learning, and agentic systems can all be understood as different instantiations of this loop. Through conceptual explanations and illustrative case studies, accompanied by optional executable examples, participants will gain a principled understanding of when adaptive control improves reasoning, where it breaks down, and what this implies for evaluation, robustness, and deployment.