From Base Model to Agent: Inside NVIDIA’s PostTraining Stack

Hosted by Aishwarya Srinivasan & Arvind Narayan and Chris Alexiuk

Thu, Aug 20, 2026

4:00 PM UTC (1 hour)

Virtual (Zoom)

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Building a career in AI as a non-coder
Aishwarya Srinivasan
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What you'll learn

Understand the Modern Post-Training Stack

See how SFT, RLVR and RLHF shape a base model into a capable assistant, reasoner and agent.

Design Rewards Models Can’t Game

Learn why strong verifiers are critical for RL, evals and reliable model improvement.

Choose the Right Model and Reasoning Budget

Learn when to use smaller vs. larger models and how reasoning depth impacts cost, latency and quality.

Think Like a Post-Training Systems Engineer

Understand how environments, rollout infrastructure and resiliency shape frontier model performance.

Why this topic matters

Modern AI capability is increasingly shaped after pre-training. This session breaks down the systems behind NVIDIA Nemotron, from SFT and RLVR to verifiers, reasoning control and infrastructure, so builders can understand how today’s reasoning and agentic models are actually trained, evaluated and improved.

You'll learn from

Aishwarya Srinivasan & Arvind Narayan

AI Engineers | Building & Teaching @ The Gen Academy

Aishwarya Srinivasan is the co-founder of The Gen Academy and one of the world's most recognized AI educators, with a community of over 1.2 million AI professionals across social platforms. Previously, she led AI Developer Relations at Fireworks AI and spent over a decade building AI products and ecosystems at Google, Microsoft, and IBM. She holds a Master's in Data Science from Columbia University. Known for breaking down complex AI concepts with clarity and honesty, Aishwarya teaches builders how to move beyond demos and develop production-ready AI systems that solve real business problems.


Arvind Narayanamurthy is the Co-founder of The Gen Academy, Previously AI Solutions Architect at Ema, and Founder of Eikos Health. He has built and deployed enterprise AI systems across Microsoft, IBM, Adobe, and high-growth startups, leading initiatives that translated machine learning into measurable business impact. Arvind holds a Master's degree from Carnegie Mellon University. His teaching focuses on first-principles thinking, practical engineering, and helping builders design, evaluate, and deploy reliable AI systems for production.

Chris Alexiuk

Product Research Engineer and Developer Advocate @ NVIDIA

Chris Alexiuk is a Product Research Engineer and Developer Advocate at NVIDIA, working at the intersection of machine learning research, generative AI, and developer education. His work spans NVIDIA’s AI ecosystem, including Nemotron, post-training, open models, and production AI systems. He also writes for the NVIDIA Technical Blog, speaks at AI conferences, and co-authored The AI Engineering Bootcamp from Wiley.

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