Hugging Face publishes a five-step curriculum for post-training AI agents
lmoroney · x · 2026-10-26
Hugging Face released a structured curriculum for post-training agents, pairing each step with a TRL trainer and using a coding agent as the running example:
- Step 1 — SFT: fine-tune on agent traces from a stronger model (SFTTrainer)
- Step 2 — Distillation: learn from a teacher's tokens or probabilities (DistillationTrainer)
- Step 3 — RL: GRPO with a reward function scoring each answer (GRPOTrainer)
- Step 4 — RL environments: train in stateful, tool-running environments via OpenEnv (GRPOTrainer)
- Step 5 — Scale: full runs of a real agent harness (AsyncGRPOTrainer)
Key insight: SFT only shows correct traces, so the model never learns to recover from its own mistakes — later steps train on the model's own attempts to fix that. Pick your starting point by what you have: good traces → step 1; automatic answer checking → step 3. Each step includes diagrams, livestreams/slides, and docs.
More from coding & agent
- Open-source 'be-ambitious' skill rewrites your prompt into an expert-level task — daniel_mac8 · 2026-10-26
- Engineer swears praising Codex ('youre doing a great job <3') gets better results — enggirlfriend · 2026-10-26
- Dev's take on personal agents: powerful with credentials, no need for cuteness — cnakazawa · 2026-10-26
- Matthew Berman: T3 wins him over with shared subs and cross-machine agents — MatthewBerman · 2026-10-26
- Yacine has LLM agents auto-generating Blender scenes as RL environments before his quota resets — yacineMTB · 2026-10-26
- FAANG engineer asks: will building AI agents in code become the new system design? — Few-Introduction5414 · 2026-10-26