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.
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TRL provides post-training methods for aligning language models with human preferences.
Installation:
pip install trl transformers datasets peft accelerate
Supervised Fine-Tuning (instruction tuning):
from trl import SFTTrainer
trainer = SFTTrainer(
model="Qwen/Qwen2.5-0.5B",
train_dataset=dataset, # Prompt-completion pairs
)
trainer.train()
DPO (align with preferences):
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()
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
Step 1: Supervised fine-tuning
Train base model on instruction-following data:
from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer, SFTConfig
from datasets import load_dataset
# Load model
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B")
# Load instruction dataset
dataset = load_dataset("trl-lib/Capybara", split="train")
# Configure training
training_args = SFTConfig(
output_dir="Qwen2.5-0.5B-SFT",
per_device_train_batch_size=4,
num_train_epochs=1,
learning_rate=2e-5,
logging_steps=10,
save_strategy="epoch"
)
# Train
trainer = SFTTrainer(
model=model,
args=training_args,
train_dataset=dataset,
tokenizer=tokenizer
)
trainer.train()
trainer.save_model()
Step 2: Train reward model
Train model to predict human preferences:
from transformers import AutoModelForSequenceClassification
from trl import RewardTrainer, RewardConfig
# Load SFT model as base
model = AutoModelForSequenceClassification.from_pretrained(
"Qwen2.5-0.5B-SFT",
num_labels=1 # Single reward score
)
tokenizer = AutoTokenizer.from_pretrained("Qwen2.5-0.5B-SFT")
# Load preference data (chosen/rejected pairs)
dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")
# Configure training
training_args = RewardConfig(
output_dir="Qwen2.5-0.5B-Reward",
per_device_train_batch_size=2,
num_train_epochs=1,
learning_rate=1e-5
)
# Train reward model
trainer = RewardTrainer(
model=model,
args=training_args,
processing_class=tokenizer,
train_dataset=dataset
)
trainer.train()
trainer.save_model()
Step 3: PPO reinforcement learning
Optimize policy using reward model:
python -m trl.scripts.ppo \
--model_name_or_path Qwen2.5-0.5B-SFT \
--reward_model_path Qwen2.5-0.5B-Reward \
--dataset_name trl-internal-testing/descriptiveness-sentiment-trl-style \
--output_dir Qwen2.5-0.5B-PPO \
--learning_rate 3e-6 \
--per_device_train_batch_size 64 \
--total_episodes 10000
Step 4: Evaluate
from transformers import pipeline
# Load aligned model
generator = pipeline("text-generation", model="Qwen2.5-0.5B-PPO")
# Test
prompt = "Explain quantum computing to a 10-year-old"
output = generator(prompt, max_length=200)[0]["generated_text"]
print(output)
Align model with preferences without reward model.
Copy this checklist:
DPO Training:
- [ ] Step 1: Prepare preference dataset
- [ ] Step 2: Configure DPO
- [ ] Step 3: Train with DPOTrainer
- [ ] Step 4: Evaluate alignment
Step 1: Prepare preference dataset
Dataset format:
{
"prompt": "What is the capital of France?",
"chosen": "The capital of France is Paris.",
"rejected": "I don't know."
}
Load dataset:
from datasets import load_dataset
dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")
# Or load your own
# dataset = load_dataset("json", data_files="preferences.json")
Step 2: Configure DPO
from trl import DPOConfig
config = DPOConfig(
output_dir="Qwen2.5-0.5B-DPO",
per_device_train_batch_size=4,
num_train_epochs=1,
learning_rate=5e-7,
beta=0.1, # KL penalty strength
max_prompt_length=512,
max_length=1024,
logging_steps=10
)
Step 3: Train with DPOTrainer
from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import DPOTrainer
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
trainer = DPOTrainer(
model=model,
args=config,
train_dataset=dataset,
processing_class=tokenizer
)
trainer.train()
trainer.save_model()
CLI alternative:
trl dpo \
--model_name_or_path Qwen/Qwen2.5-0.5B-Instruct \
--dataset_name argilla/Capybara-Preferences \
--output_dir Qwen2.5-0.5B-DPO \
--per_device_train_batch_size 4 \
--learning_rate 5e-7 \
--beta 0.1
Train with reinforcement learning using minimal memory.
Copy this checklist:
GRPO Training:
- [ ] Step 1: Define reward function
- [ ] Step 2: Configure GRPO
- [ ] Step 3: Train with GRPOTrainer
Step 1: Define reward function
def reward_function(completions, **kwargs):
"""
Compute rewards for completions.
Args:
completions: List of generated texts
Returns:
List of reward scores (floats)
"""
rewards = []
for completion in completions:
# Example: reward based on length and unique words
score = len(completion.split()) # Favor longer responses
score += len(set(completion.lower().split())) # Reward unique words
rewards.append(score)
return rewards
Or use a reward model:
from transformers import pipeline
reward_model = pipeline("text-classification", model="reward-model-path")
def reward_from_model(completions, prompts, **kwargs):
# Combine prompt + completion
full_texts = [p + c for p, c in zip(prompts, completions)]
# Get reward scores
results = reward_model(full_texts)
return [r["score"] for r in results]
Step 2: Configure GRPO
from trl import GRPOConfig
config = GRPOConfig(
output_dir="Qwen2-GRPO",
per_device_train_batch_size=4,
num_train_epochs=1,
learning_rate=1e-5,
num_generations=4, # Generate 4 completions per prompt
max_new_tokens=128
)
Step 3: Train with GRPOTrainer
from datasets import load_dataset
from trl import GRPOTrainer
# Load prompt-only dataset
dataset = load_dataset("trl-lib/tldr", split="train")
trainer = GRPOTrainer(
model="Qwen/Qwen2-0.5B-Instruct",
reward_funcs=reward_function, # Your reward function
args=config,
train_dataset=dataset
)
trainer.train()
CLI:
trl grpo \
--model_name_or_path Qwen/Qwen2-0.5B-Instruct \
--dataset_name trl-lib/tldr \
--output_dir Qwen2-GRPO \
--num_generations 4
Use TRL when:
Method selection:
Use alternatives instead:
Issue: OOM during DPO training
Reduce batch size and sequence length:
config = DPOConfig(
per_device_train_batch_size=1, # Reduce from 4
max_length=512, # Reduce from 1024
gradient_accumulation_steps=8 # Maintain effective batch
)
Or use gradient checkpointing:
model.gradient_checkpointing_enable()
Issue: Poor alignment quality
Tune beta parameter:
# 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:
config = RewardConfig(
learning_rate=1e-5, # Try different LR
num_train_epochs=3 # Train longer
)
Ensure preference dataset has clear winners:
# Verify dataset
print(dataset[0])
# Should have clear chosen > rejected
Issue: PPO training unstable
Adjust KL coefficient:
config = PPOConfig(
kl_coef=0.1, # Increase from 0.05
cliprange=0.1 # Reduce from 0.2
)
SFT training guide: See references/sft-training.md for dataset formats, chat templates, packing strategies, and multi-GPU training.
DPO variants: See references/dpo-variants.md for IPO, cDPO, RPO, and other DPO loss functions with recommended hyperparameters.
Reward modeling: See references/reward-modeling.md for outcome vs process rewards, Bradley-Terry loss, and reward model evaluation.
Online RL methods: See references/online-rl.md for PPO, GRPO, RLOO, and OnlineDPO with detailed configurations.
accelerateMemory optimization:
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Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
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Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).