Individual skill
post-training-trl-fine-tuning
Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.
cli-tool/components/skills/ai-research/post-training-trl-fine-tuning/SKILL.md
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- Last checked
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- Repository last updated
- Nov 8, 2026
- License
- MIT
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- anthropic Repository topic
- anthropic-claude Repository topic
- claude Repository topic
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- ai-agents Repository topic
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- post-training Declared skill tag
- trl Declared skill tag
- reinforcement-learning Declared skill tag
- fine-tuning Declared skill tag
- sft Declared skill tag
- dpo Declared skill tag
- ppo Declared skill tag
- grpo Declared skill tag
- rlhf Declared skill tag
- preference-alignment Declared skill tag
- huggingface Declared skill tag
- education Derived category
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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