# Closing the Loop Between AI Training and Inference with Lin Qiao - #742

Page: https://stenobird.com/podcast/twiml-ai-podcast/closing-the-loop-between-ai-training-and-inference-with-lin-qiao-742
Text version: https://stenobird.com/podcast/twiml-ai-podcast/closing-the-loop-between-ai-training-and-inference-with-lin-qiao-742.md
Podcast: [The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)](https://stenobird.com/podcast/twiml-ai-podcast)
Published: 2025-08-12T19:00:00+00:00
Episode link: https://twimlai.com/podcast/twimlai/closing-the-loop-between-ai-training-and-inference/
Audio file: https://pscrb.fm/rss/p/traffic.megaphone.fm/MLN4252780923.mp3?updated=1755024730
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JSON: https://stenobird.com/v1/public/podcasts/twiml-ai-podcast/episodes/closing-the-loop-between-ai-training-and-inference-with-lin-qiao-742
Duration seconds: 3671

## Resource

In this episode, we're joined by Lin Qiao, CEO and co-founder of Fireworks AI. Drawing on key lessons from her time building PyTorch, Lin shares her perspective on the modern generative AI development lifecycle. She explains why aligning training and inference systems is essential for creating a seamless, fast-moving production pipeline, preventing the friction that often stalls deployment. We explore the strategic shift from treating models as commodities to viewing them as core product assets. Lin details how post-training methods, like reinforcement fine-tuning (RFT), allow teams to leverage their own proprietary data to continuously improve these assets. Lin also breaks down the complex challenge of what she calls "3D optimization"—balancing cost, latency, and quality—and emphasizes the role of clear evaluation criteria to guide this process, moving beyond unreliable methods like "vibe checking." Finally, we discuss the path toward the future of AI development: designing a closed-loop system for automated model improvement, a vision made more attainable by the exciting convergence of open and closed-source model capabilities. The complete show notes for this episode can be found at https://twimlai.com/go/742.

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