Reinforcement Learning Platform Overview

class CustomerServiceReward:
    def __init__(self):
        self.criteria = {
            'resolution': 0.35,      # Did it solve the problem?
            'empathy': 0.25,         # Was it understanding?
            'clarity': 0.20,         # Was it clear?
            'efficiency': 0.10,      # Was it concise?
            'policy_compliance': 0.10 # Did it follow rules?
        }
    
    def calculate(self, response, context):
        scores = {}
        
        # Resolution scoring
        scores['resolution'] = self.check_resolution(response, context)
        
        # Empathy detection
        scores['empathy'] = self.measure_empathy(response)
        
        # Clarity assessment
        scores['clarity'] = self.evaluate_clarity(response)
        
        # Length efficiency
        scores['efficiency'] = min(1.0, 50 / len(response.split()))
        
        # Policy adherence
        scores['policy_compliance'] = self.check_policies(response, context)
        
        # Weighted sum
        total = sum(scores[k] * self.criteria[k] for k in scores)
        
        return total, scores  # Return total and breakdown

# GRPO Configuration
actor_rollout:
  ref:
    rollout:
      n: 8  # Generate 8 responses per prompt (critical for GRPO)

data:
  train_batch_size: 32  # Number of prompts per batch

actor_rollout_ref:
  actor:
    ppo_mini_batch_size: 256  # Mini-batch for weight updates
    ppo_epochs: 4  # Update epochs per trajectory set
    clip_ratio: 0.2  # GRPO clip range for stable training
    
    # GRPO-specific settings
    use_kl_loss: true  # Enable KL regularization
    kl_loss_coef: 0.001  # KL penalty strength
    kl_loss_type: "kl"  # Options: kl, abs, mse, low_var_kl, full
    
    # Loss aggregation
    loss_agg_mode: "token-mean"  # Stable for long responses

algorithm:
  adv_estimator: "grpo"  # Use GRPO instead of GAE

from stateset.rl import GRPOTrainer, RewardFunction
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import LoraConfig, get_peft_model, TaskType
from trl import GRPOTrainer, GRPOConfig
from datasets import Dataset
from sklearn.model_selection import train_test_split

# Model setup with LoRA for efficient training
def setup_model(model_name="Qwen/Qwen2.5-7B-Instruct"):
    # Load tokenizer
    tokenizer = AutoTokenizer.from_pretrained(
        model_name, 
        trust_remote_code=True,
        padding_side="left"  # Critical for generation
    )
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token
    
    # Load model with mixed precision
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        torch_dtype=torch.float16,
        device_map='auto',
        trust_remote_code=True
    )
    
    # Add LoRA adapters for efficient training
    lora_config = LoraConfig(
        r=8,
        lora_alpha=16,
        target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
        lora_dropout=0.05,
        bias="none",
        task_type=TaskType.CAUSAL_LM,
    )
    
    model = get_peft_model(model, lora_config)
    model.print_trainable_parameters()  # Typically <1% of total
    
    return model, tokenizer

# Define comprehensive reward function
class CustomerServiceReward:
    def __init__(self, expected_responses=None):
        self.empathy_keywords = ['sorry', 'understand', 'help', 'happy', 'glad', 'assist']
        self.action_keywords = ['visit', 'email', 'click', 'check', 'provide']
        self.expected_responses = expected_responses or {}
        
    def compute_reward(self, query, response, expected=None):
        response_lower = response.lower()
        
        # Similarity to expected response (if available)
        similarity_reward = 0.0
        if expected:
            import difflib
            similarity = difflib.SequenceMatcher(None, response_lower, expected.lower()).ratio()
            similarity_reward = similarity
        
        # Empathy score
        empathy_score = sum(1 for word in self.empathy_keywords if word in response_lower) / len(self.empathy_keywords)
        
        # Action-oriented score
        action_score = sum(1 for word in self.action_keywords if word in response_lower) / len(self.action_keywords)
        
        # Length penalty (concise but complete)
        word_count = len(response.split())
        if word_count < 10:
            length_penalty = -0.5
        elif word_count > 100:
            length_penalty = -0.3
        else:
            length_penalty = 0.0
        
        # Weighted combination
        total_reward = (
            0.4 * similarity_reward +
            0.3 * empathy_score +
            0.2 * action_score +
            0.1 * (1.0 + length_penalty)
        )
        
        return {
            "total": total_reward,
            "similarity": similarity_reward,
            "empathy": empathy_score,
            "action": action_score,
            "length_penalty": length_penalty
        }

# Configure GRPO training
training_config = GRPOConfig(
    output_dir="./checkpoints/grpo",
    per_device_train_batch_size=2,
    gradient_accumulation_steps=4,  # Effective batch size = 8
    learning_rate=1e-5,
    num_train_epochs=1,
    max_grad_norm=0.5,
    warmup_steps=50,
    fp16=True,  # Mixed precision training
    logging_steps=10,
    save_steps=100,
    eval_steps=50,
    
    # GRPO specific parameters
    beta=0.0,  # KL penalty coefficient
    num_generations=4,  # Generate 4 responses per prompt
    num_iterations=1,
    
    # Generation parameters
    max_prompt_length=128,
    max_completion_length=128,
    temperature=0.7,
    top_p=0.9,
    
    # Reproducibility
    seed=42,
)

# Train with proper data splitting
def train_customer_service_model(data, eval_split=0.1):
    # Split data
    train_data, eval_data = train_test_split(
        data, 
        test_size=eval_split, 
        random_state=42,
        stratify=[d['task_type'] for d in data]  # Maintain task distribution
    )
    
    # Create datasets
    train_dataset = Dataset.from_list([
        {"prompt": f"Customer: {d['query']}\nAssistant:"}
        for d in train_data
    ])
    eval_dataset = Dataset.from_list([
        {"prompt": f"Customer: {d['query']}\nAssistant:"}
        for d in eval_data
    ])
    
    # Setup reward function with expected responses
    reward_model = CustomerServiceReward({
        f"Customer: {d['query']}\nAssistant:": d['expected_response']
        for d in train_data
    })
    
    def reward_fn(completions, prompts):
        rewards = []
        for completion, prompt in zip(completions, prompts):
            expected = reward_model.expected_responses.get(prompt)
            reward_dict = reward_model.compute_reward(
                prompt.split("Customer: ")[1].split("\nAssistant:")[0],
                completion,
                expected
            )
            rewards.append(reward_dict["total"])
        return rewards
    
    # Initialize trainer
    trainer = GRPOTrainer(
        model=model,
        args=training_config,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        reward_funcs=reward_fn,
    )
    
    # Train
    trainer.train()
    
    return trainer.model

# Complete training pipeline with evaluation
import logging
from pathlib import Path
import torch
import wandb

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.StreamHandler(),
        logging.FileHandler('grpo_training.log')
    ]
)
logger = logging.getLogger(__name__)

# Environment configuration
CONFIG = {
    'max_examples': 5000,
    'batch_size': 2,
    'gradient_accumulation_steps': 4,
    'learning_rate': 1e-5,
    'num_epochs': 1,
    'max_grad_norm': 0.5,
    'warmup_steps': 50,
    'checkpoint_dir': './checkpoints/grpo',
    'use_mixed_precision': True,
    'model_name': 'Qwen/Qwen2.5-7B-Instruct',
    'max_length': 256,
    'max_new_tokens': 128,
    'lora_r': 8,
    'lora_alpha': 16,
    'lora_dropout': 0.05,
    'num_generations': 4,
    'eval_split_size': 0.1,
    'seed': 42,
}

# Set random seeds for reproducibility
def set_seed(seed: int):
    import random
    import numpy as np
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)

# Post-training evaluation
def run_post_training_evaluation(model, tokenizer, eval_examples, reward_model, num_samples=5):
    """Evaluate model performance on held-out examples"""
    logger.info("Running post-training evaluation...")
    
    eval_results = []
    for i, example in enumerate(eval_examples[:num_samples]):
        prompt = f"Customer: {example['query']}\nAssistant:"
        inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=CONFIG['max_length'])
        inputs = {k: v.to(model.device) for k, v in inputs.items()}
        
        with torch.no_grad():
            outputs = model.generate(
                **inputs,
                max_new_tokens=CONFIG['max_new_tokens'],
                temperature=0.7,
                top_p=0.9,
                pad_token_id=tokenizer.pad_token_id,
                eos_token_id=tokenizer.eos_token_id,
            )
        
        generated = tokenizer.decode(outputs[0][len(inputs['input_ids'][0]):], skip_special_tokens=True)
        reward_dict = reward_model.compute_reward(example['query'], generated, example['expected_response'])
        
        eval_results.append({
            'query': example['query'],
            'generated': generated,
            'expected': example['expected_response'],
            'reward': reward_dict
        })
        
        logger.info(f"Sample {i+1}:")
        logger.info(f"  Query: {example['query']}")
        logger.info(f"  Generated: {generated[:100]}...")
        logger.info(f"  Reward: {reward_dict['total']:.4f} (similarity: {reward_dict['similarity']:.4f})")
    
    return eval_results

# Main training function
def main():
    # Initialize wandb for experiment tracking
    wandb.init(
        project="customer-service-grpo",
        config=CONFIG,
        name=f"grpo-{datetime.now().strftime('%Y%m%d-%H%M%S')}"
    )
    
    # Set seed
    set_seed(CONFIG['seed'])
    
    # Load and prepare data
    data = load_customer_service_data()  # Your data loading function
    
    # Train model
    model = train_customer_service_model(data, eval_split=CONFIG['eval_split_size'])
    
    # Save model
    model.save_pretrained(CONFIG['checkpoint_dir'])
    tokenizer.save_pretrained(CONFIG['checkpoint_dir'])
    
    # Run evaluation
    eval_results = run_post_training_evaluation(
        model, tokenizer, eval_data, reward_model, 
        num_samples=5
    )
    
    # Log results to wandb
    for i, result in enumerate(eval_results):
        wandb.log({
            f"eval/sample_{i}/reward": result['reward']['total'],
            f"eval/sample_{i}/similarity": result['reward']['similarity'],
        })
    
    logger.info("✨ Training completed successfully!")

# Real-time metrics during training
trainer.on_step = lambda metrics: print(f"""
Step {metrics.step}:
  Average Reward: {metrics.avg_reward:.3f}
  Reward Std: {metrics.reward_std:.3f}
  KL Divergence: {metrics.kl_div:.4f}
  Response Quality: {metrics.quality_score:.2%}
""")

# Track detailed metrics with wandb
wandb.log({
    "train/reward_mean": metrics.avg_reward,
    "train/reward_std": metrics.reward_std,
    "train/kl_divergence": metrics.kl_div,
    "train/learning_rate": trainer.optimizer.param_groups[0]['lr'],
    "train/gradient_norm": metrics.grad_norm,
})

# Memory-efficient training with gradient accumulation
trainer = GRPOTrainer(
    gradient_accumulation_steps=4,  # Simulate larger batch
    gradient_checkpointing=True,    # Trade compute for memory
    fp16=True,                      # Mixed precision
    dataloader_num_workers=4,       # Parallel data loading
)

# Multi-GPU training
if torch.cuda.device_count() > 1:
    # Automatically handled by trainer with proper config
    training_config.ddp_find_unused_parameters = False
    training_config.dataloader_num_workers = 4

# Load trained model for inference
def load_trained_model(checkpoint_dir):
    from peft import PeftModel
    
    # Load base model
    base_model = AutoModelForCausalLM.from_pretrained(
        CONFIG['model_name'],
        torch_dtype=torch.float16,
        device_map='auto'
    )
    
    # Load LoRA weights
    model = PeftModel.from_pretrained(base_model, checkpoint_dir)
    model = model.merge_and_unload()  # Merge for faster inference
    
    # Load tokenizer
    tokenizer = AutoTokenizer.from_pretrained(checkpoint_dir)
    
    return model, tokenizer

# Inference function
def generate_response(model, tokenizer, query):
    prompt = f"Customer: {query}\nAssistant:"
    inputs = tokenizer(prompt, return_tensors="pt", truncation=True)
    inputs = {k: v.to(model.device) for k, v in inputs.items()}
    
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=128,
            temperature=0.7,
            top_p=0.9,
            do_sample=True,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=tokenizer.eos_token_id,
        )
    
    response = tokenizer.decode(outputs[0][len(inputs['input_ids'][0]):], skip_special_tokens=True)
    return response.strip()

# Measure improvement
baseline_model = load_model("base")
grpo_model = load_model("grpo_trained")

metrics = evaluate_models(baseline_model, grpo_model, test_set)

print(f"""
Performance Improvement:
- Task Success: +{metrics.success_delta:.1%}
- User Satisfaction: +{metrics.satisfaction_delta:.1%}
- Response Quality: +{metrics.quality_delta:.1%}
- Efficiency: +{metrics.efficiency_delta:.1%}
""")

class MultiObjectiveReward:
    def __init__(self, objectives):
        self.objectives = objectives
    
    def compute(self, response, context):
        scores = {}
        for name, (weight, function) in self.objectives.items():
            scores[name] = function(response, context) * weight
        
        # Pareto optimization
        if self.is_pareto_optimal(scores):
            bonus = 0.2
        else:
            bonus = 0
        
        return sum(scores.values()) + bonus

# Start with easy tasks, gradually increase difficulty
curriculum = [
    {"difficulty": "easy", "epochs": 1, "reward_scale": 1.0},
    {"difficulty": "medium", "epochs": 2, "reward_scale": 0.8},
    {"difficulty": "hard", "epochs": 3, "reward_scale": 0.6}
]

for stage in curriculum:
    model = trainer.train(
        data=filter_by_difficulty(data, stage["difficulty"]),
        epochs=stage["epochs"],
        reward_scale=stage["reward_scale"]
    )

# Setup for multi-GPU training
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP

def setup_distributed_training():
    """Initialize distributed training environment"""
    local_rank = int(os.environ.get("LOCAL_RANK", -1))
    world_size = int(os.environ.get("WORLD_SIZE", 1))
    
    if local_rank != -1:
        # Initialize process group
        torch.cuda.set_device(local_rank)
        dist.init_process_group(backend="nccl")
        logger.info(f"Initialized distributed training: rank {local_rank}/{world_size}")
        
        # Only log from main process
        if local_rank != 0:
            logging.getLogger().setLevel(logging.WARNING)
    
    return local_rank, world_size

# Launch distributed training
# torchrun --nproc_per_node=4 train_grpo.py

# Robust data loading with fallback
def load_data_with_fallback(file_path: str, max_examples: int = None):
    """Load data with automatic fallback to sample data"""
    
    def get_fallback_data():
        """Return sample data for testing/development"""
        return [
            {
                "messages": [
                    {"role": "user", "content": "Where is my order?"},
                    {"role": "assistant", "content": "I'd be happy to help track your order. Could you please provide your order number?"}
                ]
            },
            # Add more examples...
        ]
    
    # Try loading actual data
    if not os.path.exists(file_path):
        logger.warning(f"Data file not found: {file_path}, using fallback data")
        return get_fallback_data()
    
    try:
        data = []
        with open(file_path, 'r', encoding='utf-8') as f:
            for line in f:
                if line.strip():
                    try:
                        conv = json.loads(line)
                        if validate_conversation(conv):
                            data.append(conv)
                    except json.JSONDecodeError:
                        continue
                    
                if max_examples and len(data) >= max_examples:
                    break
        
        if not data:
            logger.warning("No valid data loaded, using fallback")
            return get_fallback_data()
            
        return data
        
    except Exception as e:
        logger.error(f"Error loading data: {e}")
        return get_fallback_data()

# Model loading with automatic fallback
class ModelManager:
    def __init__(self, model_name: str, fallback_model: str = "gpt2"):
        self.model_name = model_name
        self.fallback_model = fallback_model
        
    def load_model_and_tokenizer(self):
        try:
            # Try loading requested model
            return self._load_model(self.model_name)
        except Exception as e:
            logger.error(f"Failed to load {self.model_name}: {e}")
            if self.model_name != self.fallback_model:
                logger.info(f"Falling back to {self.fallback_model}")
                return self._load_model(self.fallback_model)
            else:
                raise

# Comprehensive GRPO configuration with all options
advanced_config = GRPOConfig(
    # Basic training parameters
    output_dir="./checkpoints/grpo",
    per_device_train_batch_size=2,
    gradient_accumulation_steps=4,
    learning_rate=1e-5,
    num_train_epochs=1,
    
    # Optimization
    max_grad_norm=0.5,
    warmup_steps=50,
    weight_decay=0.01,
    adam_epsilon=1e-8,
    
    # Mixed precision & performance
    fp16=True,
    bf16=False,  # Use FP16 instead of BF16
    gradient_checkpointing=True,
    
    # Logging & checkpointing
    logging_steps=10,
    save_steps=100,
    eval_steps=50,
    save_total_limit=3,
    load_best_model_at_end=True,
    metric_for_best_model="eval_reward",
    
    # GRPO specific
    beta=0.0,  # KL penalty (0 = no penalty)
    num_generations=4,  # Responses per prompt
    num_iterations=1,  # GRPO iterations per batch
    
    # Generation config
    max_prompt_length=128,
    max_completion_length=128,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
    
    # Advanced options
    use_liger_loss=False,  # Experimental loss function
    ddp_find_unused_parameters=False,  # For multi-GPU
    dataloader_num_workers=4,
    
    # Reproducibility
    seed=42,
    data_seed=42,
)

# Dynamic configuration based on hardware
def get_optimal_config():
    """Automatically configure based on available hardware"""
    config = GRPOConfig()
    
    # Adjust batch size based on GPU memory
    if torch.cuda.is_available():
        gpu_memory = torch.cuda.get_device_properties(0).total_memory
        if gpu_memory > 40 * 1024**3:  # 40GB+ (A100)
            config.per_device_train_batch_size = 8
            config.gradient_accumulation_steps = 1
        elif gpu_memory > 20 * 1024**3:  # 20GB+ (A6000)
            config.per_device_train_batch_size = 4
            config.gradient_accumulation_steps = 2
        else:  # Smaller GPUs
            config.per_device_train_batch_size = 1
            config.gradient_accumulation_steps = 8
    
    # Enable mixed precision on capable GPUs
    if torch.cuda.is_available() and torch.cuda.get_device_capability()[0] >= 7:
        config.fp16 = True
    
    return config

# Comprehensive experiment tracking
class ExperimentTracker:
    def __init__(self, project_name: str, use_wandb: bool = True):
        self.project_name = project_name
        self.use_wandb = use_wandb and self._init_wandb()
        self.metrics_history = []
        
    def _init_wandb(self):
        try:
            import wandb
            wandb.login(key=os.getenv('WANDB_API_KEY'))
            wandb.init(
                project=self.project_name,
                config=CONFIG,
                name=f"grpo-{datetime.now().strftime('%Y%m%d-%H%M%S')}",
                tags=["grpo", "customer-service", CONFIG['model_name'].split('/')[-1]]
            )
            return True
        except Exception as e:
            logger.warning(f"Wandb init failed: {e}")
            return False
    
    def log_metrics(self, step: int, metrics: dict):
        """Log training metrics"""
        self.metrics_history.append({"step": step, **metrics})
        
        if self.use_wandb:
            import wandb
            wandb.log(metrics, step=step)
        
        # Also log to file for backup
        with open("metrics.jsonl", "a") as f:
            f.write(json.dumps({"step": step, **metrics}) + "\n")
    
    def log_generation_samples(self, samples: list):
        """Log example generations"""
        if self.use_wandb:
            import wandb
            table = wandb.Table(columns=["Query", "Generated", "Expected", "Reward"])
            for sample in samples:
                table.add_data(
                    sample['query'],
                    sample['generated'],
                    sample['expected'],
                    sample['reward']['total']
                )
            wandb.log({"generation_samples": table})
    
    def create_summary_report(self):
        """Generate training summary"""
        if not self.metrics_history:
            return
        
        import pandas as pd
        df = pd.DataFrame(self.metrics_history)
        
        summary = {
            "total_steps": len(df),
            "final_reward": df['reward'].iloc[-1],
            "max_reward": df['reward'].max(),
            "avg_reward": df['reward'].mean(),
            "reward_improvement": df['reward'].iloc[-1] - df['reward'].iloc[0],
        }
        
        logger.info("Training Summary:")
        for key, value in summary.items():
            logger.info(f"  {key}: {value:.4f}")
        
        return summary

# Production deployment with optimization
class ProductionDeployment:
    def __init__(self, checkpoint_dir: str):
        self.checkpoint_dir = checkpoint_dir
        
    def prepare_for_deployment(self):
        """Optimize model for production inference"""
        from peft import PeftModel
        import torch.quantization as quantization
        
        # Load model
        base_model = AutoModelForCausalLM.from_pretrained(
            CONFIG['model_name'],
            torch_dtype=torch.float16,
            device_map='auto'
        )
        
        # Merge LoRA weights
        model = PeftModel.from_pretrained(base_model, self.checkpoint_dir)
        model = model.merge_and_unload()
        
        # Optional: Quantization for faster inference
        if CONFIG.get('quantize_for_deployment', False):
            model = quantization.quantize_dynamic(
                model, {torch.nn.Linear}, dtype=torch.qint8
            )
        
        # Save optimized model
        output_dir = f"{self.checkpoint_dir}_production"
        model.save_pretrained(output_dir)
        
        # Also save tokenizer
        tokenizer = AutoTokenizer.from_pretrained(self.checkpoint_dir)
        tokenizer.save_pretrained(output_dir)
        
        return output_dir
    
    def create_inference_api(self, model_path: str):
        """Create FastAPI endpoint for model inference"""
        from fastapi import FastAPI
        from pydantic import BaseModel
        
        app = FastAPI()
        
        # Load model once at startup
        model, tokenizer = load_trained_model(model_path)
        
        class Query(BaseModel):
            text: str
            max_length: int = 128
            temperature: float = 0.7
        
        @app.post("/generate")
        async def generate(query: Query):
            response = generate_response(
                model, tokenizer, query.text,
                max_length=query.max_length,
                temperature=query.temperature
            )
            return {"response": response}
        
        return app

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
pip install transformers peft datasets trl scikit-learn wandb fastapi uvicorn

# Clone the training template
git clone https://github.com/stateset/grpo-agent
cd grpo-agent

# Create and activate virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Linux/Mac
# venv\Scripts\activate  # On Windows

# Install dependencies
pip install -r requirements.txt

# Prepare your data
# Data should be in JSONL format with conversations:
# {"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}
python scripts/prepare_data.py --input your_raw_data.json --output data/training_data.jsonl --max-examples 5000 --validate

# Configure training
# Edit config.yaml or set environment variables
export MODEL_NAME="Qwen/Qwen2.5-7B-Instruct"
export BATCH_SIZE=2
export LEARNING_RATE=1e-5
export MAX_EXAMPLES=5000
export EVAL_SPLIT=0.1
export WANDB_API_KEY="your_key_here"  # Optional

# Run training
# Use torchrun for multi-GPU: torchrun --nproc_per_node=2 train_grpo.py
python train_grpo.py --data data/training_data.jsonl --config config.yaml

# Monitor training
# View logs: tail -f grpo_training.log
# If using wandb: open the link provided in console

# Evaluate results
python evaluate.py --checkpoint checkpoints/grpo/final_model --num-samples 10 --output eval_results.json

# Deploy as API
python deploy.py --model checkpoints/grpo/final_model --port 8080 --host 0.0.0.0
# Test: curl -X POST http://localhost:8080/generate -H "Content-Type: application/json" -d '{"text": "Where is my order?"}'

class MultiObjectiveReward:
    def __init__(self, objectives):
        self.objectives = objectives
    
    def compute(self, response, context):
        scores = {}
        for name, (weight, function) in self.objectives.items():
            scores[name] = function(response, context) * weight
        
        # Pareto optimization
        if self.is_pareto_optimal(scores):
            bonus = 0.2
        else:
            bonus = 0
        
        return sum(scores.values()) + bonus

# Start with easy tasks, gradually increase difficulty
curriculum = [
    {"difficulty": "easy", "epochs": 1, "reward_scale": 1.0},
    {"difficulty": "medium", "epochs": 2, "reward_scale": 0.8},
    {"difficulty": "hard", "epochs": 3, "reward_scale": 0.6}
]

for stage in curriculum:
    model = trainer.train(
        data=filter_by_difficulty(data, stage["difficulty"]),
        epochs=stage["epochs"],
        reward_scale=stage["reward_scale"]
    )

# Setup for multi-GPU training
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP

def setup_distributed_training():
    """Initialize distributed training environment"""
    local_rank = int(os.environ.get("LOCAL_RANK", -1))
    world_size = int(os.environ.get("WORLD_SIZE", 1))
    
    if local_rank != -1:
        # Initialize process group
        torch.cuda.set_device(local_rank)
        dist.init_process_group(backend="nccl")
        logger.info(f"Initialized distributed training: rank {local_rank}/{world_size}")
        
        # Only log from main process
        if local_rank != 0:
            logging.getLogger().setLevel(logging.WARNING)
    
    return local_rank, world_size

# Launch distributed training
# torchrun --nproc_per_node=4 train_grpo.py

# Robust data loading with fallback
def load_data_with_fallback(file_path: str, max_examples: int = None):
    """Load data with automatic fallback to sample data"""
    
    def get_fallback_data():
        """Return sample data for testing/development"""
        return [
            {
                "messages": [
                    {"role": "user", "content": "Where is my order?"},
                    {"role": "assistant", "content": "I'd be happy to help track your order. Could you please provide your order number?"}
                ]
            },
            # Add more examples...
        ]
    
    # Try loading actual data
    if not os.path.exists(file_path):
        logger.warning(f"Data file not found: {file_path}, using fallback data")
        return get_fallback_data()
    
    try:
        data = []
        with open(file_path, 'r', encoding='utf-8') as f:
            for line in f:
                if line.strip():
                    try:
                        conv = json.loads(line)
                        if validate_conversation(conv):
                            data.append(conv)
                    except json.JSONDecodeError:
                        continue
                    
                if max_examples and len(data) >= max_examples:
                    break
        
        if not data:
            logger.warning("No valid data loaded, using fallback")
            return get_fallback_data()
            
        return data
        
    except Exception as e:
        logger.error(f"Error loading data: {e}")
        return get_fallback_data()

# Model loading with automatic fallback
class ModelManager:
    def __init__(self, model_name: str, fallback_model: str = "gpt2"):
        self.model_name = model_name
        self.fallback_model = fallback_model
        
    def load_model_and_tokenizer(self):
        try:
            # Try loading requested model
            return self._load_model(self.model_name)
        except Exception as e:
            logger.error(f"Failed to load {self.model_name}: {e}")
            if self.model_name != self.fallback_model:
                logger.info(f"Falling back to {self.fallback_model}")
                return self._load_model(self.fallback_model)
            else:
                raise

# Comprehensive GRPO configuration with all options
advanced_config = GRPOConfig(
    # Basic training parameters
    output_dir="./checkpoints/grpo",
    per_device_train_batch_size=2,
    gradient_accumulation_steps=4,
    learning_rate=1e-5,
    num_train_epochs=1,
    
    # Optimization
    max_grad_norm=0.5,
    warmup_steps=50,
    weight_decay=0.01,
    adam_epsilon=1e-8,
    
    # Mixed precision & performance
    fp16=True,
    bf16=False,  # Use FP16 instead of BF16
    gradient_checkpointing=True,
    
    # Logging & checkpointing
    logging_steps=10,
    save_steps=100,
    eval_steps=50,
    save_total_limit=3,
    load_best_model_at_end=True,
    metric_for_best_model="eval_reward",
    
    # GRPO specific
    beta=0.0,  # KL penalty (0 = no penalty)
    num_generations=4,  # Responses per prompt
    num_iterations=1,  # GRPO iterations per batch
    
    # Generation config
    max_prompt_length=128,
    max_completion_length=128,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
    
    # Advanced options
    use_liger_loss=False,  # Experimental loss function
    ddp_find_unused_parameters=False,  # For multi-GPU
    dataloader_num_workers=4,
    
    # Reproducibility
    seed=42,
    data_seed=42,
)

# Dynamic configuration based on hardware
def get_optimal_config():
    """Automatically configure based on available hardware"""
    config = GRPOConfig()
    
    # Adjust batch size based on GPU memory
    if torch.cuda.is_available():
        gpu_memory = torch.cuda.get_device_properties(0).total_memory
        if gpu_memory > 40 * 1024**3:  # 40GB+ (A100)
            config.per_device_train_batch_size = 8
            config.gradient_accumulation_steps = 1
        elif gpu_memory > 20 * 1024**3:  # 20GB+ (A6000)
            config.per_device_train_batch_size = 4
            config.gradient_accumulation_steps = 2
        else:  # Smaller GPUs
            config.per_device_train_batch_size = 1
            config.gradient_accumulation_steps = 8
    
    # Enable mixed precision on capable GPUs
    if torch.cuda.is_available() and torch.cuda.get_device_capability()[0] >= 7:
        config.fp16 = True
    
    return config

# Comprehensive experiment tracking
class ExperimentTracker:
    def __init__(self, project_name: str, use_wandb: bool = True):
        self.project_name = project_name
        self.use_wandb = use_wandb and self._init_wandb()
        self.metrics_history = []
        
    def _init_wandb(self):
        try:
            import wandb
            wandb.login(key=os.getenv('WANDB_API_KEY'))
            wandb.init(
                project=self.project_name,
                config=CONFIG,
                name=f"grpo-{datetime.now().strftime('%Y%m%d-%H%M%S')}",
                tags=["grpo", "customer-service", CONFIG['model_name'].split('/')[-1]]
            )
            return True
        except Exception as e:
            logger.warning(f"Wandb init failed: {e}")
            return False
    
    def log_metrics(self, step: int, metrics: dict):
        """Log training metrics"""
        self.metrics_history.append({"step": step, **metrics})
        
        if self.use_wandb:
            import wandb
            wandb.log(metrics, step=step)
        
        # Also log to file for backup
        with open("metrics.jsonl", "a") as f:
            f.write(json.dumps({"step": step, **metrics}) + "\n")
    
    def log_generation_samples(self, samples: list):
        """Log example generations"""
        if self.use_wandb:
            import wandb
            table = wandb.Table(columns=["Query", "Generated", "Expected", "Reward"])
            for sample in samples:
                table.add_data(
                    sample['query'],
                    sample['generated'],
                    sample['expected'],
                    sample['reward']['total']
                )
            wandb.log({"generation_samples": table})
    
    def create_summary_report(self):
        """Generate training summary"""
        if not self.metrics_history:
            return
        
        import pandas as pd
        df = pd.DataFrame(self.metrics_history)
        
        summary = {
            "total_steps": len(df),
            "final_reward": df['reward'].iloc[-1],
            "max_reward": df['reward'].max(),
            "avg_reward": df['reward'].mean(),
            "reward_improvement": df['reward'].iloc[-1] - df['reward'].iloc[0],
        }
        
        logger.info("Training Summary:")
        for key, value in summary.items():
            logger.info(f"  {key}: {value:.4f}")
        
        return summary

# Production deployment with optimization
class ProductionDeployment:
    def __init__(self, checkpoint_dir: str):
        self.checkpoint_dir = checkpoint_dir
        
    def prepare_for_deployment(self):
        """Optimize model for production inference"""
        from peft import PeftModel
        import torch.quantization as quantization
        
        # Load model
        base_model = AutoModelForCausalLM.from_pretrained(
            CONFIG['model_name'],
            torch_dtype=torch.float16,
            device_map='auto'
        )
        
        # Merge LoRA weights
        model = PeftModel.from_pretrained(base_model, self.checkpoint_dir)
        model = model.merge_and_unload()
        
        # Optional: Quantization for faster inference
        if CONFIG.get('quantize_for_deployment', False):
            model = quantization.quantize_dynamic(
                model, {torch.nn.Linear}, dtype=torch.qint8
            )
        
        # Save optimized model
        output_dir = f"{self.checkpoint_dir}_production"
        model.save_pretrained(output_dir)
        
        # Also save tokenizer
        tokenizer = AutoTokenizer.from_pretrained(self.checkpoint_dir)
        tokenizer.save_pretrained(output_dir)
        
        return output_dir
    
    def create_inference_api(self, model_path: str):
        """Create FastAPI endpoint for model inference"""
        from fastapi import FastAPI
        from pydantic import BaseModel
        
        app = FastAPI()
        
        # Load model once at startup
        model, tokenizer = load_trained_model(model_path)
        
        class Query(BaseModel):
            text: str
            max_length: int = 128
            temperature: float = 0.7
        
        @app.post("/generate")
        async def generate(query: Query):
            response = generate_response(
                model, tokenizer, query.text,
                max_length=query.max_length,
                temperature=query.temperature
            )
            return {"response": response}
        
        return app

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
pip install transformers peft datasets trl scikit-learn wandb fastapi uvicorn

# Clone the training template
git clone https://github.com/stateset/grpo-agent
cd grpo-agent

# Create and activate virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Linux/Mac
# venv\Scripts\activate  # On Windows

# Install dependencies
pip install -r requirements.txt

# Prepare your data
# Data should be in JSONL format with conversations:
# {"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}
python scripts/prepare_data.py --input your_raw_data.json --output data/training_data.jsonl --max-examples 5000 --validate

# Configure training
# Edit config.yaml or set environment variables
export MODEL_NAME="Qwen/Qwen2.5-7B-Instruct"
export BATCH_SIZE=2
export LEARNING_RATE=1e-5
export MAX_EXAMPLES=5000
export EVAL_SPLIT=0.1
export WANDB_API_KEY="your_key_here"  # Optional

# Run training
# Use torchrun for multi-GPU: torchrun --nproc_per_node=2 train_grpo.py
python train_grpo.py --data data/training_data.jsonl --config config.yaml

# Monitor training
# View logs: tail -f grpo_training.log
# If using wandb: open the link provided in console

# Evaluate results
python evaluate.py --checkpoint checkpoints/grpo/final_model --num-samples 10 --output eval_results.json

# Deploy as API
python deploy.py --model checkpoints/grpo/final_model --port 8080 --host 0.0.0.0
# Test: curl -X POST http://localhost:8080/generate -H "Content-Type: application/json" -d '{"text": "Where is my order?"}'

# Clone the training template
git clone https://github.com/stateset/grpo-agent
cd grpo-agent

# Install dependencies
pip install transformers peft datasets trl scikit-learn wandb

# Prepare your data (JSONL format with conversations)
python prepare_data.py --input your_data.json --output training_data.jsonl

# Configure environment
export MODEL_NAME="Qwen/Qwen2.5-7B-Instruct"
export BATCH_SIZE=2
export LEARNING_RATE=1e-5
export MAX_EXAMPLES=5000
export WANDB_API_KEY="your_key_here"

# Run training
python train_grpo.py --data training_data.jsonl

# Evaluate results
python evaluate.py --checkpoint ./checkpoints/grpo/final_model

# Deploy model
python deploy.py --model ./checkpoints/grpo/final_model --port 8080