Automating the SLM Development Loop: Inside the Pioneer Agent Paper with Yash Sharma of Neurometric AI

In this episode of Inference Time Tactics, Cooper sits down with Yash Sharma, Head of AI Research at Neurometric AI, to break down the Pioneer Agent paper from Fastino Labs—a system that uses Claude Sonnet as an ML engineer in a box to build and improve small language models end-to-end. From cold start data curation to production failure diagnosis, the paper argues the real bottleneck in SLM creation isn't training—it's everything around it.   We talked about:   What Pioneer Agent actually is and why Fastino built a 25-page system paper instead of just a training paper.  How Neurometric is already tackling the same problems—and where the paper maps onto our approach.  Why naive retraining on production data quietly degrades your model—and how an agentic loop fixes it.  What the benchmarks reveal about where SLMs work out of the box versus where they need intervention.  Where Pioneer Agent hits its limits—and how Neurometric is pushing SLMs toward harder agentic tasks. Connect with Neurometric: Website: https://www.neurometric.ai/  Substack: https://neurometric.substack.com/  X: https://x.com/neurometric/  Bluesky: https://bsky.app/profile/neurometric.bsky.social   Host/s: Calvin Cooper https://x.com/cooper_nyc_  https://www.linkedin.com/in/coopernyc   Guest/s: Yash Sharma https://x.com/yash_j_sharma  https://www.linkedin.com/in/yashjsharma