Fine Tuning With Trl

TRL - Transformer Reinforcement Learning

Quick start

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()

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

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)

Workflow 2: Simple preference alignment with DPO

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

Workflow 3: Memory-efficient online RL with GRPO

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

When to use vs alternatives

Use TRL when:

• Need to align model with human preferences
• Have preference data (chosen/rejected pairs)
• Want to use reinforcement learning (PPO, GRPO)
• Need reward model training
• Doing RLHF (full pipeline)

Method selection:

• SFT: Have prompt-completion pairs, want basic instruction following
• DPO: Have preferences, want simple alignment (no reward model needed)
• PPO: Have reward model, need maximum control over RL
• GRPO: Memory-constrained, want online RL
• Reward Model: Building RLHF pipeline, need to score generations

Use alternatives instead:

• HuggingFace Trainer: Basic fine-tuning without RL
• Axolotl: YAML-based training configuration
• LitGPT: Educational, minimal fine-tuning
• Unsloth: Fast LoRA training

Common issues

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
)

Advanced topics

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.

Hardware requirements

• GPU: NVIDIA (CUDA required)
• VRAM: Depends on model and method
• SFT 7B: 16GB (with LoRA)
• DPO 7B: 24GB (stores reference model)
• PPO 7B: 40GB (policy + reward model)
• GRPO 7B: 24GB (more memory efficient)
• Multi-GPU: Supported via accelerate
• Mixed precision: BF16 recommended (A100/H100)

Memory optimization:

• Use LoRA/QLoRA for all methods
• Enable gradient checkpointing
• Use smaller batch sizes with gradient accumulation

Resources

• Docs: https://huggingface.co/docs/trl/
• GitHub: https://github.com/huggingface/trl
• Papers:
• "Training language models to follow instructions with human feedback" (InstructGPT, 2022)
• "Direct Preference Optimization: Your Language Model is Secretly a Reward Model" (DPO, 2023)
• "Group Relative Policy Optimization" (GRPO, 2024)
• Examples: https://github.com/huggingface/trl/tree/main/examples/scripts

DPO Variants

Complete guide to Direct Preference Optimization loss variants in TRL.

Overview

DPO optimizes models using preference data (chosen/rejected pairs). TRL supports 10+ loss variants for different scenarios.

Loss Types

1. Sigmoid (Standard DPO)

Formula: -log(sigmoid(β * logits))

When to use: Default choice, general preference alignment

Config:

DPOConfig(
    loss_type="sigmoid",
    beta=0.1,  # KL penalty
    per_device_train_batch_size=64,
    learning_rate=1e-6
)

2. IPO (Identity Policy Optimization)

Formula: (logits - 1/(2β))²

When to use: Better theoretical foundation, reduce overfitting

Config:

DPOConfig(
    loss_type="ipo",
    beta=0.1,
    per_device_train_batch_size=90,
    learning_rate=1e-2
)

3. Hinge (SLiC)

Formula: ReLU(1 - β * logits)

When to use: Margin-based objective

Config:

DPOConfig(
    loss_type="hinge",
    beta=0.1,
    per_device_train_batch_size=512,
    learning_rate=1e-4
)

4. Robust DPO

Formula: Sigmoid with label smoothing for noise robustness

When to use: Noisy preference labels

Config:

DPOConfig(
    loss_type="robust",
    beta=0.01,
    label_smoothing=0.1,  # Noise probability
    per_device_train_batch_size=16,
    learning_rate=1e-3,
    max_prompt_length=128,
    max_length=512
)

5. BCO Pair (Binary Classification)

Formula: Train binary classifier (chosen=1, rejected=0)

When to use: Pairwise preference data

Config:

DPOConfig(
    loss_type="bco_pair",
    beta=0.01,
    per_device_train_batch_size=128,
    learning_rate=5e-7,
    max_prompt_length=1536,
    max_completion_length=512
)

6. SPPO Hard

Formula: Push chosen→0.5, rejected→-0.5

When to use: Nash equilibrium, sparse data

Config:

DPOConfig(
    loss_type="sppo_hard",
    beta=0.1
)

7. DiscoPOP

Formula: Log-Ratio Modulated Loss

When to use: Automated loss discovery

Config:

DPOConfig(
    loss_type="discopop",
    beta=0.05,
    discopop_tau=0.05,
    per_device_train_batch_size=64,
    learning_rate=5e-7
)

8. APO Zero

Formula: Increase chosen, decrease rejected likelihood

When to use: Model worse than winning outputs

Config:

DPOConfig(
    loss_type="apo_zero",
    beta=0.1,
    per_device_train_batch_size=64,
    learning_rate=2e-7,
    max_prompt_length=512,
    max_completion_length=512
)

9. APO Down

Formula: Decrease both, emphasize rejected reduction

When to use: Model better than winning outputs

Config:

DPOConfig(
    loss_type="apo_down",
    beta=0.1,
    # Same hyperparameters as apo_zero
)

10. AOT & AOT Pair

Formula: Distributional alignment via stochastic dominance

When to use:

• aot_pair: Paired preference data
• aot: Unpaired data

Config:

DPOConfig(
    loss_type="aot_pair",  # or "aot"
    beta=0.1,
    label_smoothing=0.0
)

Multi-Loss Training

Combine multiple losses:

DPOConfig(
    loss_type=["sigmoid", "ipo"],
    loss_weights=[0.7, 0.3],  # Weighted combination
    beta=0.1
)

Key Parameters

Beta (β)

Controls deviation from reference model:

• Higher (0.5): More conservative, stays close to reference
• Lower (0.01): More aggressive alignment
• Default: 0.1

Label Smoothing

For robust DPO:

• 0.0: No smoothing (default)
• 0.1-0.3: Moderate noise robustness
• 0.5: Maximum noise tolerance

Max Lengths

• max_prompt_length: 128-1536
• max_completion_length: 128-512
• max_length: Total sequence (1024-2048)

Comparison Table

References

• DPO paper: https://arxiv.org/abs/2305.18290
• IPO paper: https://arxiv.org/abs/2310.12036
• TRL docs: https://huggingface.co/docs/trl/dpo_trainer

Online RL Methods

Guide to online reinforcement learning with PPO, GRPO, RLOO, and OnlineDPO.

Overview

Online RL generates completions during training and optimizes based on rewards.

PPO (Proximal Policy Optimization)

Classic RL algorithm for LLM alignment.

Basic Usage

python -m trl.scripts.ppo \
    --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct \
    --reward_model_path reward-model \
    --dataset_name trl-internal-testing/descriptiveness-sentiment-trl-style \
    --output_dir model-ppo \
    --learning_rate 3e-6 \
    --per_device_train_batch_size 64 \
    --total_episodes 10000 \
    --num_ppo_epochs 4 \
    --kl_coef 0.05

Key Parameters

• kl_coef: KL penalty (0.05-0.2)
• num_ppo_epochs: Epochs per batch (2-4)
• cliprange: PPO clip (0.1-0.3)
• vf_coef: Value function coef (0.1)

GRPO (Group Relative Policy Optimization)

Memory-efficient online RL.

Basic Usage

from trl import GRPOTrainer, GRPOConfig
from datasets import load_dataset

# Define reward function
def reward_func(completions, **kwargs):
    return [len(set(c.split())) for c in completions]

config = GRPOConfig(
    output_dir="model-grpo",
    num_generations=4,  # Completions per prompt
    max_new_tokens=128
)

trainer = GRPOTrainer(
    model="Qwen/Qwen2-0.5B-Instruct",
    reward_funcs=reward_func,
    args=config,
    train_dataset=load_dataset("trl-lib/tldr", split="train")
)
trainer.train()

Key Parameters

• num_generations: 2-8 completions
• max_new_tokens: 64-256
• Learning rate: 1e-5 to 1e-4

Memory Comparison

References

• PPO paper: https://arxiv.org/abs/1707.06347
• GRPO paper: https://arxiv.org/abs/2402.03300
• TRL docs: https://huggingface.co/docs/trl/

Reward Modeling

Guide to training reward models with TRL for RLHF pipelines.

Overview

Reward models score completions based on human preferences. Used in:

• PPO training (RL feedback)
• GRPO online RL
• Completion ranking

Basic Training

from transformers import AutoModelForSequenceClassification, AutoTokenizer
from trl import RewardTrainer, RewardConfig
from datasets import load_dataset

# Load model (num_labels=1 for single reward score)
model = AutoModelForSequenceClassification.from_pretrained(
    "Qwen/Qwen2.5-0.5B-Instruct",
    num_labels=1
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

# Load preference dataset (chosen/rejected pairs)
dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")

# Configure
config = RewardConfig(
    output_dir="Qwen2.5-Reward",
    per_device_train_batch_size=2,
    num_train_epochs=1,
    learning_rate=1e-5
)

# Train
trainer = RewardTrainer(
    model=model,
    args=config,
    processing_class=tokenizer,
    train_dataset=dataset
)
trainer.train()

Dataset Format

Required fields:

{
  "prompt": "Question or instruction",
  "chosen": "Better response",
  "rejected": "Worse response"
}

Bradley-Terry Loss

Default loss function:

loss = -log(sigmoid(reward_chosen - reward_rejected))

Learns to score chosen > rejected.

Using Reward Models

Inference

from transformers import pipeline

# Load trained reward model
reward_pipe = pipeline("text-classification", model="Qwen2.5-Reward")

# Score completions
texts = ["Good answer", "Bad answer"]
scores = reward_pipe(texts)
print(scores)  # Higher score = better

In PPO

from trl import PPOTrainer, PPOConfig

config = PPOConfig(
    reward_model_path="Qwen2.5-Reward"  # Use trained reward model
)

trainer = PPOTrainer(
    model=policy_model,
    config=config,
    # Reward model loaded automatically
)

Hyperparameters

Evaluation

Check reward separation:

# Chosen should score higher than rejected
chosen_rewards = model(**chosen_inputs).logits
rejected_rewards = model(**rejected_inputs).logits

accuracy = (chosen_rewards > rejected_rewards).float().mean()
print(f"Accuracy: {accuracy:.2%}")  # Target: >80%

References

• InstructGPT paper: https://arxiv.org/abs/2203.02155
• TRL docs: https://huggingface.co/docs/trl/reward_trainer

SFT Training Guide

Complete guide to Supervised Fine-Tuning (SFT) with TRL for instruction tuning and task-specific fine-tuning.

Overview

SFT trains models on input-output pairs to minimize cross-entropy loss. Use for:

• Instruction following
• Task-specific fine-tuning
• Chatbot training
• Domain adaptation

Dataset Formats

Format 1: Prompt-Completion

[
  {
    "prompt": "What is the capital of France?",
    "completion": "The capital of France is Paris."
  }
]

Format 2: Conversational (ChatML)

[
  {
    "messages": [
      {"role": "user", "content": "What is Python?"},
      {"role": "assistant", "content": "Python is a programming language."}
    ]
  }
]

Format 3: Text-only

[
  {"text": "User: Hello\nAssistant: Hi! How can I help?"}
]

Basic Training

from trl import SFTTrainer, SFTConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
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 dataset
dataset = load_dataset("trl-lib/Capybara", split="train")

# Configure
config = SFTConfig(
    output_dir="Qwen2.5-SFT",
    per_device_train_batch_size=4,
    num_train_epochs=1,
    learning_rate=2e-5,
    save_strategy="epoch"
)

# Train
trainer = SFTTrainer(
    model=model,
    args=config,
    train_dataset=dataset,
    tokenizer=tokenizer
)
trainer.train()

Chat Templates

Apply chat templates automatically:

trainer = SFTTrainer(
    model=model,
    args=config,
    train_dataset=dataset,  # Messages format
    tokenizer=tokenizer
    # Chat template applied automatically
)

Or manually:

def format_chat(example):
    messages = example["messages"]
    text = tokenizer.apply_chat_template(messages, tokenize=False)
    return {"text": text}

dataset = dataset.map(format_chat)

Packing for Efficiency

Pack multiple sequences into one to maximize GPU utilization:

config = SFTConfig(
    packing=True,  # Enable packing
    max_seq_length=2048,
    dataset_text_field="text"
)

Benefits: 2-3× faster training Trade-off: Slightly more complex batching

Multi-GPU Training

accelerate launch --num_processes 4 train_sft.py

Or with config:

config = SFTConfig(
    output_dir="model-sft",
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    num_train_epochs=1
)

LoRA Fine-Tuning

from peft import LoraConfig

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules="all-linear",
    lora_dropout=0.05,
    task_type="CAUSAL_LM"
)

trainer = SFTTrainer(
    model=model,
    args=config,
    train_dataset=dataset,
    peft_config=lora_config  # Add LoRA
)

Hyperparameters

References

• TRL docs: https://huggingface.co/docs/trl/sft_trainer
• Examples: https://github.com/huggingface/trl/tree/main/examples/scripts