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
Installs just this skill. Get the whole plugin for auto-invocation.
⚡ How it fires
How this skill gets triggered: by you, by Claude, or both.
Fires itselfClaude auto-loads it when your prompt matches the work.
You can call itInvoke it directly when you want it.
Slash command/post-training-trl-fine-tuning
👁️ Context preview
The summary Claude sees to decide when to auto-load this skill.
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
📊 Stats
Stars29,856
Forks3,189
LanguagePython
LicenseMIT
📦 Ships with claude-code-templates
</> SKILL.md
post-training-trl-fine-tuning.SKILL.md
---name: fine-tuning-with-trl
description: 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.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Post-Training, TRL, Reinforcement Learning, Fine-Tuning, SFT, DPO, PPO, GRPO, RLHF, Preference Alignment, HuggingFace]
dependencies: [trl, transformers, datasets, peft, accelerate, torch]
---# 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()
```
# Higher beta = more conservative (stays closer to reference)
config = DPOConfig(beta=0.5) # Default 0.1
# Lower beta = more aggressive alignment
config = DPOConfig(beta=0.01)
```
**Issue: Reward model not learning**
Check loss type and learning rate:
```python
config = RewardConfig(
learning_rate=1e-5, # Try different LR
num_train_epochs=3 # Train longer
)
```
Ensure preference dataset has clear winners:
```python
# Verify dataset
print(dataset[0])
# Should have clear chosen > rejected
```
**Issue: PPO training unstable**
Adjust KL coefficient:
```python
config = PPOConfig(
kl_coef=0.1, # Increase from 0.05
cliprange=0.1 # Reduce from 0.2
)
```
## Advanced topics
**SFT training guide**: See [references/sft-training.md](references/sft-training.md) for dataset formats, chat templates, packing strategies, and multi-GPU training.
**DPO variants**: See [references/dpo-variants.md](references/dpo-variants.md) for IPO, cDPO, RPO, and other DPO loss functions with recommended hyperparameters.
**Reward modeling**: See [references/reward-modeling.md](references/reward-modeling.md) for outcome vs process rewards, Bradley-Terry loss, and reward model evaluation.
**Online RL methods**: See [references/online-rl.md](references/online-rl.md) for PPO, GRPO, RLOO, and OnlineDPO with detailed configurations.
