Everything I Learned Training Frontier Small Models#

Maxime Labonne

• Why: Small models that reliably follow instructions and call tools while running on-device under 1 GB change where inference happens — off GPU clusters and onto edge devices — which matters for anyone planning AI infrastructure capacity.
• What: A post-training playbook for frontier small models using the LFM2.5 recipe: on-policy preference alignment, agentic RL, and curriculum training with iterative model merging — plus the failure modes specific to 1B scale ("doom loops", capability interference) and how to fix them. Targeted at structured data extraction and multi-turn tool use.
• How: Staged post-training (preference alignment → agentic RL → curriculum with iterative model merging) tuned for the constraints of sub-1B models.
• Where applicable: Fine-tuning and deploying small models for concrete use cases; recipe-level guidance transfers, though the exact recipe is tied to the LFM2.5 stack. Abstract-level only — expect depth in the talk itself.