Fine Tuning With Trl

TRL: SFT, DPO, PPO, GRPO, reward modeling for LLM RLHF.

Data, AI & Research|v1|Updated 7/2/2026|GitHub source

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# TRL - Transformer Reinforcement Learning

## Quick start

TRL provides post-training methods for aligning language models with human preferences.

**Installation**:
```bash
pip install trl transformers datasets peft accelerate
```

**Supervised Fine-Tuning** (instruction tuning):
```python
from trl import SFTTrainer

trainer = SFTTrainer(
    model="Qwen/Qwen2.5-0.5B",
    train_dataset=dataset,  # Prompt-completion pairs
)
trainer.train()
```

**DPO** (align with preferences):
```python
from trl import DPOTrainer, DPOConfig

config = DPOConfig(output_dir="model-dpo", beta=0.1)
trainer = DPOTrainer(
    model=model,
    args=config,
    train_dataset=preference_dataset,  # chosen/rejected pairs
    processing_class=tokenizer
)
trainer.train()
```

## Common workflows

### Workflow 1: Full RLHF pipeline (SFT → Reward Model → PPO)

Complete pipeline from base model to human-aligned model.

Copy this checklist:

```
RLHF Training:
- [ ] Step 1: Supervised fine-tuning (SFT)
- [ ] Step 2: Train reward model
- [ ] Step 3: PPO reinforcement learning
- [ ] Step 4: Evaluate aligned model

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