A curriculum for post-training agents

Agentic post-training adapts pretrained models for multi-turn tool use and task solving through a five-step curriculum based on Hugging Face’s Training Agents series. It begins with supervised fine-tuning on successful agent traces, which is inexpensive and stable but does not teach recovery from mistakes, then moves to distillation from a stronger teacher model, providing dense token-level guidance but limiting students to teacher capabilities. Reinforcement learning with GRPO instead scores the model’s own outputs, rewarding attempts that outperform others in a sampled group, though its answer-level feedback requires more rollouts. RL environments extend this process to stateful, multi-turn tasks in which agents use tools and receive rewards based on final outcomes, such as passing coding tests. The final stage trains agents through real harnesses and remote sandboxes, separating generation and training across GPUs so models learn under deployment-like conditions while supporting long-running episodes at scale.

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