Optimization Grpo

GRPO/RL Fine-Tuning

🎯 Trigger Conditions

Use when asked about GRPO (Group Relative Policy Optimization), RL fine-tuning for LLMs, or reinforcement learning for DSPy.

📚 Prerequisites

• trl package installed
• transformers package installed
• GPU available (recommended)
• Training data prepared

🛠️ GRPO Implementation

1. Basic GRPO Setup

from trl import GRPOConfig, GRPOTrainer
from transformers import AutoModelForCausalLM, AutoTokenizer

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

# Configure GRPO
config = GRPOConfig(
    learning_rate=1e-5,
    num_train_epochs=3,
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    output_dir="grpo-output"
)

# Create trainer
trainer = GRPOTrainer(
    model=model,
    config=config,
    train_dataset=your_dataset,
    tokenizer=tokenizer
)

# Train
trainer.train()

2. GRPO with Reward Model

from trl import GRPOConfig, GRPOTrainer

# Define reward function
def reward_fn(completions, **kwargs):
    rewards = []
    for completion in completions:
        # Calculate reward based on quality
        reward = calculate_quality(completion)
        rewards.append(reward)
    return rewards

# Create trainer with reward
trainer = GRPOTrainer(
    model=model,
    config=config,
    train_dataset=your_dataset,
    tokenizer=tokenizer,
    reward_fn=reward_fn
)

3. GRPO for DSPy

import dspy

# Create DSPy program
program = dspy.ChainOfThought("question -> reasoning -> answer")

# Prepare training data
train_data = [
    dspy.Example(
        question="What is X?",
        answer="Y"
    ).with_inputs("question")
]

# Fine-tune with GRPO
trainer = GRPOTrainer(
    model=program.model,
    config=grpo_config,
    train_dataset=train_data,
    reward_fn=your_reward_function
)

trainer.train()

4. GRPO Hyperparameter Tuning

config = GRPOConfig(
    # Learning rate schedule
    learning_rate=1e-5,
    lr_scheduler_type="cosine",
    
    # Training parameters
    num_train_epochs=3,
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    
    # GRPO-specific
    beta=0.1,  # KL divergence coefficient
    gamma=0.99,  # Reward discount factor
    
    # Regularization
    weight_decay=0.01,
    max_grad_norm=1.0
)

⚠️ Pitfalls

• Reward design: Poor rewards lead to poor policies
• Compute cost: GRPO requires significant resources
• Stability: RL can be unstable
• Overfitting: Monitor validation performance

📖 References

• TRL GRPO
• GRPO Paper