specific method · filed under post-training
Three-stage post-training with multi-teacher on-policy distillation
A sequence of supervised fine-tuning, reinforcement learning for domain specialists, and multi-teacher on-policy distillation to consolidate their policies.
Also called three-stage post-training paradigm.
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Our post-training pipeline follows a three-stage paradigm: initializing baseline agent capabilities via supervised fine-tuning (SFT), developing specialized domain experts at varying reasoning effort via Reinforcement Learning (RL), and consolidating these domain-specific policies into a single model using Multi-Teacher On-Policy Distillation (MOPD).
Filed alongside
Other methods under post-training.