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trl-training
Post-train LLMs with TRL (Transformers Reinforcement Learning) — SFT, DPO, GRPO, KTO, and reward-model training. Use when writing or debugging training code with the TRL Python API or the trl CLI.
skills/trl-training/SKILL.md
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# TRL
Each method pairs a `*Trainer` class with a `*Config` dataclass. Configs extend `transformers.TrainingArguments`, so all of its arguments work in any trainer config.
| Trainer | Dataset type |
|---|---|
| `SFTTrainer` | language modeling or prompt-completion |
| `DPOTrainer` | preference (chosen/rejected pairs) |
| `GRPOTrainer` | prompt-only + reward function(s) |
| `DistillationTrainer` | prompt-only + a teacher model (on-policy distillation) |
| `KTOTrainer` | unpaired preference (per-sample bool label) |
| `RewardTrainer` | preference (chosen/rejected pairs); trains a scalar reward model, not a policy |
Many more trainers (OnlineDPO, ORPO, CPO, GKD, …) live in `trl.experimental` with unstable APIs: https://huggingface.co/docs/trl/experimental_overview