GRPO/RL Training with TRL

Expert-level guidance for implementing Group Relative Policy Optimization (GRPO) using the Transformer Reinforcement Learning (TRL) library. This skill provides battle-tested patterns, critical insights, and production-ready workflows for fine-tuning language models with custom reward functions.

When to Use This Skill

Use GRPO training when you need to:

• Enforce specific output formats (e.g., XML tags, JSON, structured reasoning)
• Teach verifiable tasks with objective correctness metrics (math, coding, fact-checking)
• Improve reasoning capabilities by rewarding chain-of-thought patterns
• Align models to domain-specific behaviors without labeled preference data
• Optimize for multiple objectives simultaneously (format + correctness + style)

Do NOT use GRPO for:

• Simple supervised fine-tuning tasks (use SFT instead)
• Tasks without clear reward signals
• When you already have high-quality preference pairs (use DPO/PPO instead)

Core Concepts

1. GRPO Algorithm Fundamentals

Key Mechanism:

• Generates multiple completions for each prompt (group size: 4-16)
• Compares completions within each group using reward functions
• Updates policy to favor higher-rewarded responses relative to the group

Critical Difference from PPO:

• No separate reward model needed
• More sample-efficient (learns from within-group comparisons)
• Simpler to implement and debug

Mathematical Intuition:

For each prompt p:
  1. Generate N completions: {c₁, c₂, ..., cₙ}
  2. Compute rewards: {r₁, r₂, ..., rₙ}
  3. Learn to increase probability of high-reward completions
     relative to low-reward ones in the same group

2. Reward Function Design Philosophy

Golden Rules:

1. Compose multiple reward functions - Each handles one aspect (format, correctness, style)
2. Scale rewards appropriately - Higher weight = stronger signal
3. Use incremental rewards - Partial credit for partial compliance
4. Test rewards independently - Debug each reward function in isolation

Reward Function Types:

Implementation Workflow

Step 1: Dataset Preparation

Critical Requirements:

• Prompts in chat format (list of dicts with 'role' and 'content')
• Include system prompts to set expectations
• For verifiable tasks, include ground truth answers as additional columns

Example Structure:

from datasets import load_dataset, Dataset

SYSTEM_PROMPT = """
Respond in the following format:
<reasoning>
[Your step-by-step thinking]
</reasoning>
<answer>
[Final answer]
</answer>
"""

def prepare_dataset(raw_data):
    """
    Transform raw data into GRPO-compatible format.

    Returns: Dataset with columns:
    - 'prompt': List[Dict] with role/content (system + user messages)
    - 'answer': str (ground truth, optional but recommended)
    """
    return raw_data.map(lambda x: {
        'prompt': [
            {'role': 'system', 'content': SYSTEM_PROMPT},
            {'role': 'user', 'content': x['question']}
        ],
        'answer': extract_answer(x['raw_answer'])
    })

Pro Tips:

• Use one-shot or few-shot examples in system prompt for complex formats
• Keep prompts concise (max_prompt_length: 256-512 tokens)
• Validate data quality before training (garbage in = garbage out)

Step 2: Reward Function Implementation

Template Structure:

def reward_function_name(
    prompts,        # List[List[Dict]]: Original prompts
    completions,    # List[List[Dict]]: Model generations
    answer=None,    # Optional: Ground truth from dataset
    **kwargs        # Additional dataset columns
) -> list[float]:
    """
    Evaluate completions and return rewards.

    Returns: List of floats (one per completion)
    """
    # Extract completion text
    responses = [comp[0]['content'] for comp in completions]

    # Compute rewards
    rewards = []
    for response in responses:
        score = compute_score(response)
        rewards.append(score)

    return rewards

Example 1: Correctness Reward (Math/Coding)

def correctness_reward(prompts, completions, answer, **kwargs):
    """Reward correct answers with high score."""
    responses = [comp[0]['content'] for comp in completions]
    extracted = [extract_final_answer(r) for r in responses]
    return [2.0 if ans == gt else 0.0
            for ans, gt in zip(extracted, answer)]

Example 2: Format Reward (Structured Output)

import re

def format_reward(completions, **kwargs):
    """Reward XML-like structured format."""
    pattern = r'<reasoning>.*?</reasoning>\s*<answer>.*?</answer>'
    responses = [comp[0]['content'] for comp in completions]
    return [1.0 if re.search(pattern, r, re.DOTALL) else 0.0
            for r in responses]

Example 3: Incremental Format Reward (Partial Credit)

def incremental_format_reward(completions, **kwargs):
    """Award partial credit for format compliance."""
    responses = [comp[0]['content'] for comp in completions]
    rewards = []

    for r in responses:
        score = 0.0
        if '<reasoning>' in r:
            score += 0.25
        if '</reasoning>' in r:
            score += 0.25
        if '<answer>' in r:
            score += 0.25
        if '</answer>' in r:
            score += 0.25
        # Penalize extra text after closing tag
        if r.count('</answer>') == 1:
            extra_text = r.split('</answer>')[-1].strip()
            score -= len(extra_text) * 0.001
        rewards.append(score)

    return rewards

Critical Insight: Combine 3-5 reward functions for robust training. Order matters less than diversity of signals.

Step 3: Training Configuration

Memory-Optimized Config (Small GPU)

from trl import GRPOConfig

training_args = GRPOConfig(
    output_dir="outputs/grpo-model",

    # Learning rate
    learning_rate=5e-6,          # Lower = more stable
    adam_beta1=0.9,
    adam_beta2=0.99,
    weight_decay=0.1,
    warmup_ratio=0.1,
    lr_scheduler_type='cosine',

    # Batch settings
    per_device_train_batch_size=1,
    gradient_accumulation_steps=4,  # Effective batch = 4

    # GRPO-specific
    num_generations=8,            # Group size: 8-16 recommended
    max_prompt_length=256,
    max_completion_length=512