Grpo Rl Training

---
name: grpo-rl-training
description: Guides GRPO (Group Relative Policy Optimization) fine-tuning of language models with the TRL library, including GRPOTrainer configuration, composing multiple reward functions (correctness, format, length, style), dataset prep in chat format, Unsloth setup, LoRA merging, and monitoring reward, reward_std and KL. Use when training a model to follow a strict output format such as XML or JSON. Use when teaching verifiable tasks like math or code with objective correctness rewards. Use when improving chain-of-thought reasoning with custom reward functions. Use when debugging flat rewards, mode collapse, or OOM in GRPO runs. Do not use for plain supervised fine-tuning (use SFT) or when high-quality preference pairs exist (use DPO or PPO).
license: MIT
metadata:
  version: 1.0.0
  category: ml-training
  maintainer: Kalaris Labs
  tags: Post-Training, Reinforcement Learning, GRPO, TRL, RLHF, Reward Modeling, Reasoning, DPO, PPO, Structured Output
  dependencies: transformers>=4.47.0, trl>=0.14.0, datasets>=3.2.0, peft>=0.14.0, torch
---

# 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:**

| Type | Use Case | Example Weight |
|------|----------|----------------|
| **Correctness** | Verifiable tasks (math, code) | 2.0 (highest) |
| **Format** | Strict structure enforcement | 0.5-1.0 |
| **Length** | Encourage verbosity/conciseness | 0.1-0.5 |
| **Style** | Penalize unwanted patterns | -0.5 to 0.5 |

---

## 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:**
```python
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:**
```python
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)**
```python
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)**
```python
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)**
```python
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)**
```python
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,

    # Training duration
    num_train_epochs=1,
    max_steps=None,               # Or set fixed steps (e.g., 500)

    # Optimization
    bf16=True,                    # Faster on A100/H100
    optim="adamw_8bit",          # Memory-efficient optimizer
    max_grad_norm=0.1,

    # Logging
    logging_steps=1,
    save_steps=100,
    report_to="wandb",            # Or "none" for no logging
)
```

**High-Performance Config (Large GPU)**
```python
training_args = GRPOConfig(
    output_dir="outputs/grpo-model",
    learning_rate=1e-5,
    per_device_train_batch_size=4,
    gradient_accumulation_steps=2,
    num_generations=16,           # Larger groups = better signal
    max_prompt_length=512,
    max_completion_length=1024,
    num_train_epochs=1,
    bf16=True,
    use_vllm=True,                # Fast generation with vLLM
    logging_steps=10,
)
```

**Critical Hyperparameters:**

| Parameter | Impact | Tuning Advice |
|-----------|--------|---------------|
| `num_generations` | Group size for comparison | Start with 8, increase to 16 if GPU allows |
| `learning_rate` | Convergence speed/stability | 5e-6 (safe), 1e-5 (faster, riskier) |
| `max_completion_length` | Output verbosity | Match your task (512 for reasoning, 256 for short answers) |
| `gradient_accumulation_steps` | Effective batch size | Increase if GPU memory limited |

### Step 4: Model Setup and Training

**Standard Setup (Transformers)**
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import LoraConfig
from trl import GRPOTrainer

# Load model
model_name = "Qwen/Qwen2.5-1.5B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",  # 2-3x faster
    device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_token

# Optional: LoRA for parameter-efficient training
peft_config = LoraConfig(
    r=16,                         # Rank (higher = more capacity)
    lora_alpha=32,               # Scaling factor (typically 2*r)
    target_modules=[
        "q_proj", "k_proj", "v_proj", "o_proj",
        "gate_proj", "up_proj", "down_proj"
    ],
    task_type="CAUSAL_LM",
    lora_dropout=0.05,
)

# Initialize trainer
trainer = GRPOTrainer(
    model=model,
    processing_class=tokenizer,
    reward_funcs=[
        incremental_format_reward,
        format_reward,
        correctness_reward,
    ],
    args=training_args,
    train_dataset=dataset,
    peft_config=peft_config,      # Remove for full fine-tuning
)

# Train
trainer.train()

# Save
trainer.save_model("final_model")
```

**Unsloth Setup (2-3x Faster)**
```python
from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="google/gemma-3-1b-it",
    max_seq_length=1024,
    load_in_4bit=True,
    fast_inference=True,
    max_lora_rank=32,
)

model = FastLanguageModel.get_peft_model(
    model,
    r=32,
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
                    "gate_proj", "up_proj", "down_proj"],
    lora_alpha=32,
    use_gradient_checkpointing="unsloth",
)

# Rest is identical to standard setup
trainer = GRPOTrainer(model=model, ...)
trainer.train()
```

---

## Critical Training Insights

Details, code examples and parameter tables: [references/critical-training-insights.md](references/critical-training-insights.md). Read it when this step applies.

## Advanced Patterns

Details, code examples and parameter tables: [references/advanced-patterns.md](references/advanced-patterns.md). Read it when this step applies.

## Deployment and Inference

### Save and Merge LoRA
```python
# Merge LoRA adapters into base model
if hasattr(trainer.model, 'merge_and_unload'):
    merged_model = trainer.model.merge_and_unload()
    merged_model.save_pretrained("production_model")
    tokenizer.save_pretrained("production_model")
```

### Inference Example
```python
from transformers import pipeline

generator = pipeline(
    "text-generation",
    model="production_model",
    tokenizer=tokenizer
)

result = generator(
    [
        {'role': 'system', 'content': SYSTEM_PROMPT},
        {'role': 'user', 'content': "What is 15 + 27?"}
    ],
    max_new_tokens=256,
    do_sample=True,
    temperature=0.7,
    top_p=0.9
)
print(result[0]['generated_text'])
```

---

## Best Practices Checklist

**Before Training:**
- [ ] Validate dataset format (prompts as List[Dict])
- [ ] Test reward functions on sample data
- [ ] Calculate expected max_prompt_length from data
- [ ] Choose appropriate num_generations based on GPU memory
- [ ] Set up logging (wandb recommended)

**During Training:**
- [ ] Monitor reward progression (should increase)
- [ ] Check reward_std (should stay > 0.1)
- [ ] Watch for OOM errors (reduce batch size if needed)
- [ ] Sample generations every 50-100 steps
- [ ] Validate format compliance on holdout set

**After Training:**
- [ ] Merge LoRA weights if using PEFT
- [ ] Test on diverse prompts
- [ ] Compare to baseline model
- [ ] Document reward weights and hyperparameters
- [ ] Save reproducibility config

---

## Troubleshooting Guide

### Debugging Workflow
1. **Isolate reward functions** - Test each independently
2. **Check data distribution** - Ensure diversity in prompts
3. **Reduce complexity** - Start with single reward, add gradually
4. **Monitor generations** - Print samples every N steps
5. **Validate extraction logic** - Ensure answer parsing works

### Quick Fixes
```python
# Debug reward function
def debug_reward(completions, **kwargs):
    responses = [comp[0]['content'] for comp in completions]
    for i, r in enumerate(responses[:2]):  # Print first 2
        print(f"Response {i}: {r[:200]}...")
    return [1.0] * len(responses)  # Dummy rewards

# Test without training
trainer = GRPOTrainer(..., reward_funcs=[debug_reward])
trainer.generate_completions(dataset[:1])  # Generate without updating
```

---

## References and Resources

**Official Documentation:**
- TRL GRPO Trainer: https://huggingface.co/docs/trl/grpo_trainer
- DeepSeek R1 Paper: https://arxiv.org/abs/2501.12948
- Unsloth Docs: https://docs.unsloth.ai/

**Example Repositories:**
- Open R1 Implementation: https://github.com/huggingface/open-r1
- TRL Examples: https://github.com/huggingface/trl/tree/main/examples

**Recommended Reading:**
- Progressive Disclosure Pattern for agent instructions
- Reward shaping in RL (Ng et al.)
- LoRA paper (Hu et al., 2021)

---

## Usage Instructions for Agents

When this skill is loaded:

1. **Read this entire file** before implementing GRPO training
2. **Start with the simplest reward function** (e.g., length-based) to validate setup
3. **Use the templates** in `templates/` directory as starting points
4. **Reference examples** in `examples/` for task-specific implementations
5. **Follow the workflow** sequentially (don't skip steps)
6. **Debug incrementally** - add one reward function at a time

**Critical Reminders:**
- Always use multiple reward functions (3-5 is optimal)
- Monitor reward metrics, not loss
- Test reward functions before training
- Start small (num_generations=4), scale up gradually
- Save checkpoints frequently (every 100 steps)

This skill is designed for **expert-level implementation**. Beginners should start with supervised fine-tuning before attempting GRPO.

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