The Importance of Post-Training for On-Device Agentic Models

Post-training has become a major part of building useful AI systems.
In this interview, Liquid’s post-training team, including Maxime Labonne, Edoardo Mosca, and Jiahui Wang, discuss how post-training turns base models into models with specific capabilities: Following instructions, using tools, handling long-context environments, and carrying out multi-step tasks.

They also discuss Antidoom, Liquid’s work on reducing “doom loops” in small models and why post-training is especially important for building reliable on-device agentic models.

Read our release on reducing doom loops: https://www.liquid.ai/blog/antidoom

Careers at Liquid AI: https://www.liquid.ai/careers