trl

name	trl
domain	ai-ml
description	TRL (Transformer Reinforcement Learning) for SFT, RLHF, DPO training. Use for supervised fine-tuning, reward modeling, RLHF (PPO), and Direct Preference Optimization (DPO). Best for training instruction-following LLMs. For model loading use transformers; for efficient fine-tuning use peft; for quantization use bitsandbytes.
license	Apache-2.0 license
metadata	{"skill-author":"Eric Yiru"}

TRL: Transformer Reinforcement Learning

Overview

TRL provides a full stack of tools for training language models with reinforcement learning, including SFT (Supervised Fine-Tuning), Reward Modeling, RLHF (PPO), and DPO (Direct Preference Optimization). Apply this skill for instruction tuning, RLHF training, preference learning, and alignment training.

When to Use This Skill

This skill should be used when:

• Supervised fine-tuning (SFT) on instruction data
• Training with RLHF (Reinforcement Learning from Human Feedback)
• Implementing PPO training for LLM alignment
• Direct Preference Optimization (DPO)
• Reward model training
• Preference learning
• Chat model training
• Model alignment with human preferences

Quick Start

Basic Import and Setup

from trl import SFTTrainer, DPOTrainer, RewardTrainer
from trl.trainer import PPOTrainer, RLHFTrainer
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

Supervised Fine-Tuning (SFT)

from trl import SFTTrainer
from transformers import AutoModelForCausalLM, AutoTokenizer
from datasets import load_dataset

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

# Load dataset
dataset = load_dataset("imdb", split="train[:1000]")

# Configure SFT trainer
trainer = SFTTrainer(
    model=model,
    train_dataset=dataset,
    tokenizer=tokenizer,
    dataset_text_field="text",
    max_seq_length=512,
)

# Train
trainer.train()

# Save
trainer.save_model("sft_output")

DPO (Direct Preference Optimization)

from trl import DPOTrainer
from transformers import AutoModelForCausalLM, AutoTokenizer
from datasets import load_dataset

# Load model
model = AutoModelForCausalLM.from_pretrained("gpt2")
ref_model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

# Format dataset for DPO
# Dataset should have: prompt, chosen, rejected
dataset = load_dataset("Anthropic/hh-rlhf", split="train[:1000]")

def format_dpo(example):
    return {
        "prompt": example["prompt"],
        "chosen": example["chosen"],
        "rejected": example["rejected"]
    }

dataset = dataset.map(format_dpo)

# Configure DPO trainer
dpo_trainer = DPOTrainer(
    model=model,
    ref_model=ref_model,
    train_dataset=dataset,
    tokenizer=tokenizer,
    beta=0.1,  # DPO temperature
    max_length=512,
    max_prompt_length=128,
)

# Train
dpo_trainer.train()

# Save
dpo_trainer.save_model("dpo_output")

Reward Model Training

from trl import RewardTrainer
from transformers import AutoModelForSequenceClassification
from datasets import load_dataset

# Load reward model
model = AutoModelForSequenceClassification.from_pretrained(
    "gpt2",
    num_labels=1,
)
tokenizer = AutoTokenizer.from_pretrained("gpt2")

# Load preference dataset
# Format: prompt, chosen (preferred), rejected (not preferred)
dataset = load_dataset("Anthropic/hh-rlhf", split="train[:1000]")

def format_reward(example):
    return {
        "prompt": example["prompt"],
        "chosen": example["chosen"],
        "rejected": example["rejected"]
    }

dataset = dataset.map(format_reward)

# Configure trainer
trainer = RewardTrainer(
    model=model,
    train_dataset=dataset,
    tokenizer=tokenizer,
)

# Train
trainer.train()

RLHF with PPO

Setup

from trl import PPOTrainer, RLHFTrainer
from transformers import AutoModelForCausalLM, AutoTokenizer
from datasets import load_dataset

# Load models
model = AutoModelForCausalLM.from_pretrained("gpt2")
ref_model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")
tokenizer.pad_token = tokenizer.eos_token

# Load dataset
dataset = load_dataset("Anthropic/hh-rlhf", split="train[:100]")

# Format for RLHF
def process_examples(examples):
    text = []
    for prompt, chosen in zip(examples["prompt"], examples["chosen"]):
        text.append(prompt + chosen)
    return {"text": text}

dataset = dataset.map(process_examples, batched=True)

PPOTrainer

# Configure PPO trainer
ppo_trainer = PPOTrainer(
    model=model,
    ref_model=ref_model,
    tokenizer=tokenizer,
    dataset=dataset,
    learning_rate=1.4e-5,
    batch_size=4,
    mini_batch_size=1,
    max_length=512,
    max_prompt_length=256,
)

# Generate rewards function
def reward_fn(texts):
    # Return reward scores
    # Simple example: length-based reward
    return [len(t) for t in texts]

# Training loop
for epoch in range(3):
    # Get batch
    batch = ppo_trainer.get_train_batch()

    # Generate responses
    query_tensors = [tokenizer(t, return_tensors="pt").input_ids[0] for t in batch["prompt"]]
    response_tensors = ppo_trainer.generate(
        query_tensors,
        return_prompt=False,
        length_sampler=output_length_sampler(64),
    )

    # Compute rewards
    texts = [tokenizer.decode(r.squeeze()) for r in response_tensors]
    rewards = reward_fn(texts)

    # PPO step
    stats = ppo_trainer.step(query_tensors, response_tensors, rewards)
    print(stats)

RLHFTrainer (Full Pipeline)

from trl import RLHFTrainer
from trl.trainer import RLHFConfig

# Full RLHF trainer
rlhf_trainer = RLHFTrainer(
    model=model,
    args=RLHFConfig(
        output_dir="./rlhf_output",
        num_train_epochs=3,
        per_device_train_batch_size=4,
        learning_rate=1.4e-5,
        logging_steps=10,
    ),
    train_dataset=dataset,
    tokenizer=tokenizer,
    peft_config=None,  # Can use PEFT for efficiency
)

# Train SFT first, then PPO
rlhf_trainer.train()

Training Configuration

SFTTrainer Parameters

trainer = SFTTrainer(
    model=model,
    train_dataset=dataset,
    tokenizer=tokenizer,
    dataset_text_field="text",  # Field containing text
    max_seq_length=512,
    packing=True,  # Pack sequences
    formatting_func=None,  # Custom formatting
    args=TrainingArguments(
        output_dir="./output",
        num_train_epochs=3,
        per_device_train_batch_size=4,
        learning_rate=2e-5,
    ),
    # PEFT integration
    peft_config=None,  # Can add LoraConfig here
)

DPOTrainer Parameters

dpo_trainer = DPOTrainer(
    model=model,
    ref_model=ref_model,  # Reference model for KL
    train_dataset=dataset,
    tokenizer=tokenizer,
    beta=0.1,  # Temperature for DPO
    loss_type="sigmoid",  # or "hinge"
    max_length=512,
    max_prompt_length=128,
    args=TrainingArguments(
        output_dir="./output",
        num_train_epochs=3,
        per_device_train_batch_size=4,
        learning_rate=1e-5,
    ),
)

RewardTrainer Parameters

trainer = RewardTrainer(
    model=model,
    train_dataset=dataset,
    tokenizer=tokenizer,
    loss_type="sigmoid",  # or "margin"
    args=TrainingArguments(
        output_dir="./output",
        num_train_epochs=3,
        per_device_train_batch_size=4,
    ),
)

Data Formatting

SFT Format

# Format for instruction tuning
def format_instruction(example):
    return f"""### Instruction:
{example['instruction']}

### Input:
{example['input']}

### Response:
{example['output']}"""

dataset = dataset.map(lambda x: {"text": format_instruction(x)})

DPO Format

# Required format
dataset = dataset.map(lambda x: {
    "prompt": x["prompt"],
    "chosen": x["chosen"],  # Preferred response
    "rejected": x["rejected"]  # Rejected response
})

Reward Training Format

# Required format
dataset = dataset.map(lambda x: {
    "prompt": x["prompt"],
    "chosen": x["chosen"],
    "rejected": x["rejected"]
})

Advanced Features

PEFT Integration

from peft import LoraConfig, get_peft_model
from trl import SFTTrainer, DPOTrainer

# LoRA + SFT
peft_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
)

trainer = SFTTrainer(
    model=model,
    train_dataset=dataset,
    tokenizer=tokenizer,
    peft_config=peft_config,
)

Custom Loss

# For DPO with custom loss
dpo_trainer = DPOTrainer(
    model=model,
    ref_model=ref_model,
    train_dataset=dataset,
    tokenizer=tokenizer,
    loss_type="custom",  # Define custom loss
)

Generation Config

# Customize generation during training
trainer = SFTTrainer(
    model=model,
    train_dataset=dataset,
    tokenizer=tokenizer,
    max_seq_length=512,
    dataset_text_field="text",
)

Saving and Loading

Save Model

# Save SFT model
trainer.save_model("sft_output")
tokenizer.save_pretrained("sft_output")

# Save DPO model
dpo_trainer.save_model("dpo_output")
tokenizer.save_pretrained("dpo_output")

# Save with PEFT
trainer.save_model("peft_output")  # Saves only adapter

Load for Inference

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load SFT
model = AutoModelForCausalLM.from_pretrained("sft_output")

# Load PEFT
model = PeftModel.from_pretrained(base_model, "peft_output")

# Load DPO
model = AutoModelForCausalLM.from_pretrained("dpo_output")

Common Pitfalls and Best Practices

1. Use appropriate data format: Each trainer requires specific columns
2. Set pad_token: Always set tokenizer pad_token
3. Reference model: Keep separate for DPO/PPO
4. Reward function: Design carefully for your task
5. Learning rates: Lower for RLHF than standard fine-tuning
6. KL penalty: Controls how far policy drifts from reference
7. Use PEFT: For memory efficiency with large models
8. DPO vs PPO: DPO simpler, PPO more flexible
9. Data quality: Critical for alignment training
10. Evaluation: Use held-out data for reward model

Key Parameters

SFTTrainer

• dataset_text_field: Column with text data
• max_seq_length: Maximum sequence length
• packing: Pack multiple sequences
• formatting_func: Custom formatting function
• peft_config: PEFT configuration

DPOTrainer

• beta: Temperature parameter (0.1 typical)
• ref_model: Reference model
• max_length: Maximum sequence length
• loss_type: Loss function type

PPOTrainer

• learning_rate: Learning rate for PPO
• batch_size: Batch size for PPO
• mini_batch_size: Mini batch size
• ppo_epochs: PPO epochs per batch

Additional Resources

• TRL Documentation: https://huggingface.co/docs/trl
• TRL GitHub: https://github.com/huggingface/trl
• LLM Alignment: https://arxiv.org/abs/2203.02155
• DPO Paper: https://arxiv.org/abs/2305.18290

Tips for Effective Use

1. Start with SFT: Build foundation before RLHF
2. Quality over quantity: RLHF needs good preference data
3. Monitor training: Watch for reward hacking
4. Iterate: Fine-tune reward model if needed
5. Test models: Evaluate on held-out prompts

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