In this page, we’ll learn how to implement Group Relative Policy Optimization (GRPO) using the Transformer Reinforcement Learning (TRL) library. We’ll focus on practical implementation with minimal code.
We’ll explore the core concepts of GRPO as they are embodied in TRL’s GRPOTrainer, using snippets from the official TRL documentation to guide us.
First, let’s remind ourselves of some of the important concepts of GRPO algorithm:
What do we need to do to implement GRPO?
Here’s a minimal example to get started with GRPO training:
from trl import GRPOTrainer, GRPOConfig
from datasets import load_dataset
# 1. Load your dataset
dataset = load_dataset("your_dataset", split="train")
# 2. Define a simple reward function
def reward_func(completions, **kwargs):
"""Example: Reward longer completions"""
return [float(len(completion)) for completion in completions]
# 3. Configure training
training_args = GRPOConfig(
output_dir="output",
num_train_epochs=3,
per_device_train_batch_size=4,
gradient_accumulation_steps=2,
logging_steps=10,
)
# 4. Initialize and train
trainer = GRPOTrainer(
model="your_model", # e.g. "Qwen/Qwen2-0.5B-Instruct"
args=training_args,
train_dataset=dataset,
reward_funcs=reward_func,
)
trainer.train()
Your dataset should contain prompts that the model will respond to. The GRPO trainer will generate multiple completions for each prompt and use the reward function to compare them.
The reward function is crucial - it determines how the model learns. Here are two practical examples:
# Example 1: Reward based on completion length
def reward_length(completions, **kwargs):
return [float(len(completion)) for completion in completions]
# Example 2: Reward based on matching a pattern
import re
def reward_format(completions, **kwargs):
pattern = r"^<think>.*?</think><answer>.*?</answer>$"
return [1.0 if re.match(pattern, c) else 0.0 for c in completions]
Key parameters to consider in GRPOConfig
:
training_args = GRPOConfig(
# Essential parameters
output_dir="output",
num_train_epochs=3,
num_generation=4, # Number of completions to generate for each prompt
per_device_train_batch_size=4, # We want to get all generations in one device batch
# Optional but useful
gradient_accumulation_steps=2,
learning_rate=1e-5,
logging_steps=10,
# GRPO specific (optional)
use_vllm=True, # Speed up generation
)
The num_generation
parameter is particularly important for GRPO as it defines the group size - how many different completions the model will generate for each prompt. This is a key differentiator from other RL methods:
The group size should be chosen based on your computational resources and the complexity of your task. For simple tasks, smaller groups (4-8) may be sufficient, while more complex reasoning tasks might benefit from larger groups (8-16).
per_device_train_batch_size
and gradient_accumulation_steps
based on your GPU memory.use_vllm=True
for faster generation if your model is supported.reward
: Average reward across completionsreward_std
: Standard deviation within reward groupskl
: KL divergence from reference modelThe DeepSeek R1 paper demonstrates several effective approaches to reward function design that you can adapt for your own GRPO implementation:
One of the easiest rewards to implement is a length-based reward. You can reward longer completions:
def reward_len(completions, **kwargs):
ideal_length = 20
return [-abs(ideal_length - len(completion)) for completion in completions]
This reward function penalizes completions that are too short or too long, encouraging the model to generate completions that are close to the ideal length of 20 tokens.
For tasks with objectively correct answers (like mathematics or coding), you can implement rule-based reward functions:
def problem_reward(completions, answers, **kwargs):
"""Reward function for math problems with verifiable answers
completions: list of completions to evaluate
answers: list of answers to the problems from the dataset
"""
rewards = []
for completion, correct_answer in zip(completions, answers):
# Extract the answer from the completion
try:
# This is a simplified example - you'd need proper parsing
answer = extract_final_answer(completion)
# Binary reward: 1 for correct, 0 for incorrect
reward = 1.0 if answer == correct_answer else 0.0
rewards.append(reward)
except:
# If we can't parse an answer, give a low reward
rewards.append(0.0)
return rewards
You can also reward proper formatting, which was important in the DeepSeek R1 training:
def format_reward(completions, **kwargs):
"""Reward completions that follow the desired format"""
# Example: Check if the completion follows a think-then-answer format
pattern = r"<think>(.*?)</think>\s*<answer>(.*?)</answer>"
rewards = []
for completion in completions:
match = re.search(pattern, completion, re.DOTALL)
if match:
# Check if there's substantial content in both sections
think_content = match.group(1).strip()
answer_content = match.group(2).strip()
if len(think_content) > 20 and len(answer_content) > 0:
rewards.append(1.0)
else:
rewards.append(
0.5
) # Partial reward for correct format but limited content
else:
rewards.append(0.0) # No reward for incorrect format
return rewards
These examples demonstrate how you can implement reward functions inspired by the DeepSeek R1 training process, focusing on correctness, formatting, and combined signals.
In the next section, you will follow an exercise to implement GRPO in TRL.
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