From Conventional LLMs to Reasoning Models to Agents

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Register now for the next free ACM TechTalk, "From Conventional LLMs to Reasoning Models to Agents," presented on Thursday, August 19 at 12:00 PM ET/16:00 UTC by Sebastian Raschka, Founder and Principal AI & LLM Researcher at RAIR Labs. Marlene Mhangami, Senior Developer Advocate at Microsoft, will moderate the Q&A session.

Leave your comments and questions with our speaker now and any time before the live event on ACM's Discourse Page. And check out the page after the webcast for extended discussion with your peers in the computing community, as well as further resources on reasoning models and more.

Many of us have been using and experimenting with large language models (LLMs) for several years. Then, in 2025, new techniques for training reasoning models gave LLMs a noticeable boost in problem-solving capabilities. Since then, newer generations of LLMs have been substantially more useful for complex tasks such as math and coding.

For example, in software development, the progression over the years has been from simple code completion to planning, implementing, testing, and debugging entire features and code bases.

Today, reasoning models, which can be viewed as an evolution of conventional LLMs, are also at the center of many agent frameworks.

This talk will explain how reasoning models differ from conventional LLMs, with a particular focus on inference scaling and training techniques. It will also introduce the main components of AI agents and show how reasoning models serve as the engine that enables agents to plan, use tools, evaluate intermediate results, and recover from mistakes.

Attendees will leave with practical patterns for building resilient microservices that remain stable even when failures, automation, and AI-driven decisions collide.

Duration: 60 minutes (including audience Q&A)

Presenter:
Sebastian Raschka, Founder, Principal AI & LLM Research Engineer, RAIR Labs

Sebastian Raschka is an LLM Research Engineer with more than a decade of experience in artificial intelligence. His work bridges academia and industry, including roles as senior engineer at Lightning AI and as a statistics professor at the University of Wisconsin-Madison. He is the author of Build a Reasoning Model (From Scratch) and Build a Large Language Model (From Scratch), both from Manning Publications, as well as Machine Learning with PyTorch and Scikit-Learn (Packt), and Machine Learning Q and AI (No Starch Press). His expertise lies in LLM research and the development of high-performance AI systems, with a deep focus on practical, code-driven implementations.

Moderator:
Marlene Mhangami, Senior Developer Advocate, Microsoft

Marlene is a software engineer, explorer, and speaker currently based in London. She is a Senior Developer Advocate working at Microsoft focusing on Python and AI. Marlene is a previous director and vice-chair for the Python Software Foundation and is currently serving as Co-Chair of the ACM Practitioner Board. In 2017, she co-founded ZimboPy, a non-profit organization that gives Zimbabwean young women access to resources in the field of technology. She is also the previous chair of PyCon Africa and is an advocate for women in tech on the continent.

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