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TRL: SFT, DPO, GRPO, RLOO reward modeling for LLM RLHF.

Skill metadata


Source	Optional — install with `mibyan skills install official/mlops/trl-fine-tuning`
Path	`optional-skills/mlops/training/trl-fine-tuning`
Version	`1.0.1`
Author	Orchestra Research
License	MIT
Dependencies	`trl`, `transformers`, `datasets`, `peft`, `accelerate`, `torch`
Platforms	linux, macos, windows
Tags	`Post-Training`, `TRL`, `Reinforcement Learning`, `Fine-Tuning`, `SFT`, `DPO`, `GRPO`, `RLOO`, `RLHF`, `Preference Alignment`, `HuggingFace`

Reference: full SKILL.md

The following is the complete skill definition that Mibyan loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.

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 → RLOO)

Complete pipeline from base model to human-aligned model.

Note (TRL 1.x): PPO has been removed from TRL — PPOTrainer, PPOConfig, and python -m trl.scripts.ppo no longer exist. Use an online-RL trainer TRL still ships: RLOO (RLOOTrainer / trl rloo) is the closest drop-in for a reward-model-driven RLHF pipeline, and GRPO (GRPOTrainer / trl grpo, see Workflow 3) is the memory-efficient alternative. The step below uses RLOO.

Copy this checklist:

RLHF Training:
- [ ] Step 1: Supervised fine-tuning (SFT)
- [ ] Step 2: Train reward model
- [ ] Step 3: RLOO 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,
    processing_class=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: RLOO reinforcement learning Optimize policy using the reward model. PPO was removed in TRL 1.x; use the RLOO CLI (trl rloo) with the trained reward model passed via --reward_model_name_or_path:

trl rloo \
    --model_name_or_path Qwen2.5-0.5B-SFT \
    --reward_model_name_or_path Qwen2.5-0.5B-Reward \
    --dataset_name trl-internal-testing/descriptiveness-sentiment-trl-style \
    --output_dir Qwen2.5-0.5B-RLOO \
    --learning_rate 3e-6 \
    --per_device_train_batch_size 64 \
    --num_generations 4

Equivalent Python (RLOOTrainer / RLOOConfig):

from trl import RLOOTrainer, RLOOConfig
from transformers import AutoModelForSequenceClassification, AutoTokenizer

reward_model = AutoModelForSequenceClassification.from_pretrained(
    "Qwen2.5-0.5B-Reward", num_labels=1
)

config = RLOOConfig(
    output_dir="Qwen2.5-0.5B-RLOO",
    per_device_train_batch_size=64,
    learning_rate=3e-6,
    num_generations=4,
)

trainer = RLOOTrainer(
    model="Qwen2.5-0.5B-SFT",
    reward_funcs=reward_model,   # a reward model (or a callable reward function)
    args=config,
    train_dataset=dataset,       # prompt-only dataset
    processing_class=tokenizer,
)
trainer.train()

Step 4: Evaluate

from transformers import pipeline

# Load aligned model
generator = pipeline("text-generation", model="Qwen2.5-0.5B-RLOO")

# 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. For in-depth GRPO guidance — reward function design, critical training insights (loss behavior, mode collapse, tuning), and advanced multi-stage patterns — see references/grpo-training.md. A production-ready training script is in templates/basic_grpo_training.py. 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 (RLOO, 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)
RLOO: Have a reward model, want online RL (the reward-model-driven RLHF path; PPO was removed in TRL 1.x)
GRPO: Memory-constrained, want online RL with reward functions
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: Online RL (RLOO/GRPO) training unstable Adjust the KL/beta regularization toward the reference policy:

from trl import RLOOConfig

config = RLOOConfig(
    beta=0.05,          # KL coefficient toward the reference model (increase for stability)
    num_generations=4,  # more samples per prompt = lower-variance advantage estimates
)

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. GRPO deep dive: See references/grpo-training.md for expert-level GRPO patterns — reward function design philosophy, training insights (why loss increases, mode collapse detection), hyperparameter tuning, multi-stage training, and troubleshooting. Production-ready template in templates/basic_grpo_training.py.

Hardware requirements

GPU: NVIDIA (CUDA required)
VRAM: Depends on model and method
- SFT 7B: 16GB (with LoRA)
- DPO 7B: 24GB (stores reference model)
- RLOO 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

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Trl Fine Tuning — TRL: SFT, DPO, GRPO, RLOO reward modeling for LLM RLHF

Skill metadata

Reference: full SKILL.md

TRL - Transformer Reinforcement Learning

Quick start

Common workflows

Workflow 1: Full RLHF pipeline (SFT → Reward Model → RLOO)

Workflow 2: Simple preference alignment with DPO

Workflow 3: Memory-efficient online RL with GRPO

When to use vs alternatives

Common issues

Advanced topics

Hardware requirements

Resources

​Skill metadata

​Reference: full SKILL.md

​TRL - Transformer Reinforcement Learning

​Quick start

​Common workflows

​Workflow 1: Full RLHF pipeline (SFT → Reward Model → RLOO)

​Workflow 2: Simple preference alignment with DPO

​Workflow 3: Memory-efficient online RL with GRPO

​When to use vs alternatives

​Common issues

​Advanced topics

​Hardware requirements

​Resources

Skill metadata

Reference: full SKILL.md

TRL - Transformer Reinforcement Learning

Quick start

Common workflows

Workflow 1: Full RLHF pipeline (SFT → Reward Model → RLOO)

Workflow 2: Simple preference alignment with DPO

Workflow 3: Memory-efficient online RL with GRPO

When to use vs alternatives

Common issues

Advanced topics

Hardware requirements

Resources