Everything I Learned Training Frontier Small Models — Maxime Labonne, Liquid AI
Apr 29, 2026 · 20:13
Maxime Labonne, head of post-training at Liquid AI, presents the LFM2.5 recipe for training frontier small models, arguing they require specialized approaches distinct from scaled-down big models. He details how Liquid's architecture uses gated short convolutions for latency-sensitive on-device deployment, achieving faster throughput than Gemma 3 or Qwen 3.5. Post-training stages—SFT, on-policy preference alignment, and RL—are tailored for narrow task focus like data extraction and tool use. A key challenge is 'doom loops' (repetition), which reached 15% after mid-training in a 1.2B reasoning model; solutions include preference alignment rejecting looped responses and RL with verifiable rewards and n-gram penalties, nearly eliminating the issue. He advocates combining small models with agentic tools (e.g., web search) to overcome memory limits, as they excel at reasoning and tool use despite lower knowledge capacity. The talk also covers decisions on when to use small vs. large models—latency, privacy, offline use—and notes that distillation alone likely won't fully solve doom loops.
