Individual skill
fine-tuning-with-trl
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.
06-post-training/trl-fine-tuning/SKILL.md
Source commit 773a52944ba4747a18bd4ae9ade53fff041adcbc
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- Last checked
- Nov 9, 2026
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- Repository stars (not skill ratings)
- 14,461
- Repository last updated
- Oct 11, 2026
- License
- MIT
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- ai Repository topic
- ai-research Repository topic
- claude Repository topic
- claude-code Repository topic
- claude-skills Repository topic
- codex Repository topic
- gemini Repository topic
- gpt-5 Repository topic
- grpo Repository topic
- huggingface Repository topic
- 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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Read excerpt
# 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