Frontier post-training recipe review with Finbarr Timbers
Interconnects Nathan Lambert
Post-training recipes for large language models have converged on multi-teacher on-policy distillation (MOPD) as a frontier approach in 2026, replacing earlier monolithic reinforcement learning stages with domain-specialist teachers merged into a single student model. The shift occurred because single-stage RL proved expensive and created capability conflicts across math, code, and reasoning domains, while specialist models using SFT-then-RL per domain are cheaper and organizationally scalable. This architectural change, pioneered by MiMo Flash V2 in January 2026 and scaled by DeepSeek V4 and Nemotron 3 Ultra to over 10 teachers, enables labs to expand post-training complexity beyond what single-stage RL recipes like OLMo-3 could achieve.
Why it matters
"Interview" #18
