Uploaded June 2026 | Updated September 2026, 2 weeks ago
[2026 - DAY 3 - MODEL SYSTEMS] Reinforcement Learning from Verifiable Rewards (RLVR) is increasingly common in post-training pipelines, but the practical details are often glossed over. How do you design reward functions that programmatically verify model outputs? What makes synthetic training data effective? How do you build a custom RL environment that doesn't silently break your training?
SPEAKER: Chris Alexiuk - Product Research Engineer, NVIDIA
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AI Council brings together the brightest minds in data to share industry knowledge, technical architectures and best practices in building cutting edge data & AI systems and tools.
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[2026 - DAY 3 - MODEL SYSTEMS] Reinforcement Learning from Verifiable Rewards (RLVR) is increasingly common in post-training pipelines, but the practical details are often glossed over. How do you design reward functions that programmatically verify model outputs? What makes synthetic training data effective? How do you build a custom RL environment that doesn't silently break your training?
SPEAKER: Chris Alexiuk - Product Research Engineer, NVIDIA
👉 Sign up for our "No BS" Newsletter to get the latest technical data & AI content: aicouncil.com/newsletter
ABOUT AI COUNCIL:
AI Council brings together the brightest minds in data to share industry knowledge, technical architectures and best practices in building cutting edge data & AI systems and tools.
FIND US:
Website: aicouncil.com
LinkedIn: linkedin.com/company/aicouncilconf
X: https://x.com/aicouncilconf
