GRPO Agent Framework - StateSet API Docs

Introduction

The GRPO Agent Framework is a production-ready library for training multi-turn conversational AI agents using Group Relative Policy Optimization (GRPO). This framework transforms advanced reinforcement learning techniques into an accessible platform for building sophisticated conversational agents that can handle complex, extended dialogues.

Why GRPO?

Superior Stability

GRPO provides more stable training than traditional RL methods

Multi-Turn Excellence

Native support for extended conversations with context preservation

Production Ready

Built for real-world deployment with monitoring and serving capabilities

Quick Start

Installation

npm install grpo-agent-framework

Your First Agent in 5 Minutes

import asyncio
from grpo_agent_framework import MultiTurnAgent, ConversationEnvironment, train

async def main():
    # 1. Create an agent
    agent = MultiTurnAgent.from_model("microsoft/DialoGPT-medium")
    
    # 2. Define conversation scenarios
    scenarios = [
        {"user_responses": ["Hi!", "How are you?", "Thanks!"]},
        {"user_responses": ["I need help", "My order is late", "When will it arrive?"]}
    ]
    env = ConversationEnvironment(scenarios=scenarios)
    
    # 3. Train with GRPO
    trained_agent = await train(
        agent=agent,
        environment=env,
        num_episodes=1000,
        profile="balanced"  # Auto-optimized settings
    )
    
    # 4. Use the trained agent
    response = await trained_agent.generate_response([
        {"role": "user", "content": "Hello! I have a question."}
    ])
    print(f"Agent: {response}")

asyncio.run(main())

Core Concepts

1. Agents

Agents are the conversational entities that learn through GRPO training:

class MultiTurnAgent(Agent):
    """Specialized for extended conversations"""
    
    async def process_turn(self, history, user_input, context):
        # Manages conversation state
        # Preserves context across turns
        # Generates contextually appropriate responses
        pass

Use for: Customer service, tutoring, general assistants

2. Environments

Environments simulate conversation scenarios for training:

# Built-in environments
from grpo_agent_framework import (
    ConversationEnvironment,  # Open-ended conversations
    TaskEnvironment,         # Goal-oriented interactions
    SimulatedEnvironment     # Uses external simulators
)

# Custom environment example
class CustomerServiceEnvironment(Environment):
    def __init__(self, ticket_database):
        self.tickets = ticket_database
    
    async def step(self, state, action):
        # Simulate customer interactions
        customer_response = await self.simulate_customer(state, action)
        
        # Calculate rewards based on resolution
        reward = self.calculate_service_quality(state, action, customer_response)
        
        # Check if ticket is resolved
        done = self.is_ticket_resolved(state)
        
        return new_state, customer_response, reward, done

3. Reward Functions

Reward functions guide agent learning by scoring conversation quality:

Pre-built Rewards

from grpo_agent_framework.rewards import (
    HelpfulnessReward,
    SafetyReward,
    CorrectnessReward,
    EngagementReward
)

# Combine multiple rewards
composite = CompositeReward([
    HelpfulnessReward(weight=0.4),
    SafetyReward(weight=0.3),
    CorrectnessReward(weight=0.2),
    EngagementReward(weight=0.1)
])

Custom Rewards

@reward_function(weight=0.5)
async def domain_specific_reward(turns, context):
    score = 0.0
    
    # Custom evaluation logic
    if resolved_issue(turns):
        score += 0.5
    
    if maintained_professionalism(turns):
        score += 0.3
    
    if under_time_limit(turns):
        score += 0.2
    
    return score

Training Pipeline

Basic Training

from grpo_agent_framework import train, TrainingConfig

# Simple training with defaults
trained_agent = await train(
    agent=agent,
    environment=environment,
    num_episodes=2000
)

# Advanced training with custom config
config = TrainingConfig(
    num_episodes=5000,
    batch_size=32,
    learning_rate=1e-4,
    gamma=0.95,
    clip_range=0.2,
    value_coefficient=0.5,
    entropy_coefficient=0.01,
    max_grad_norm=0.5,
    profile="aggressive",  # or "balanced", "conservative"
    auto_adjust=True,     # Automatic hyperparameter tuning
    checkpoint_interval=500,
    early_stopping_patience=10
)

trained_agent = await train(
    agent=agent,
    environment=environment,
    reward_fn=custom_reward,
    config=config,
    callbacks=[wandb_callback, checkpoint_callback]
)

Training Profiles

The framework includes pre-tuned profiles based on extensive research:

# Maximum stability, slower convergence
profile="conservative"

# Characteristics:
# - Lower learning rate (5e-5)
# - Smaller clip range (0.1)
# - Higher value coefficient (1.0)
# - More gradient clipping

# Best for:
# - Safety-critical applications
# - Initial experiments
# - Sensitive domains

Automatic Optimization

The framework includes intelligent auto-tuning:

from grpo_agent_framework import AutoTrainer

# Automatic configuration based on task analysis
auto_trainer = AutoTrainer(
    auto_adjust=True,
    target_metrics={
        'success_rate': 0.95,
        'avg_turns': 5,
        'user_satisfaction': 0.9
    }
)

# Analyzes task complexity and adjusts accordingly
trained_agent = await auto_trainer.train(
    agent=agent,
    environment=environment,
    max_time_hours=24
)

# Monitor auto-adjustments
print(f"Final config: {auto_trainer.final_config}")
print(f"Adjustments made: {auto_trainer.adjustment_history}")

Advanced Features

1. Tool Integration

Enable agents to use external tools and APIs:

from grpo_agent_framework import ToolAgent, Tool

# Define available tools
tools = [
    Tool(
        name="calculator",
        description="Performs mathematical calculations",
        function=calculate,
        parameters={
            "expression": {"type": "string", "description": "Math expression"}
        }
    ),
    Tool(
        name="search",
        description="Searches the web for information",
        function=web_search,
        parameters={
            "query": {"type": "string", "description": "Search query"}
        }
    ),
    Tool(
        name="database",
        description="Queries customer database",
        function=query_database,
        parameters={
            "sql": {"type": "string", "description": "SQL query"}
        }
    )
]

# Create tool-enabled agent
tool_agent = ToolAgent(
    model_name="gpt-3.5-turbo",
    tools=tools,
    tool_selection_strategy="adaptive"  # or "always", "conservative"
)

# Tools are automatically used during conversations
response = await tool_agent.process_turn(
    history=[{"role": "user", "content": "What's 37 * 48?"}],
    user_input="Can you calculate that for me?",
    context={}
)
# Agent uses calculator tool and responds with result

2. Multi-GPU Training

Scale training across multiple GPUs:

from grpo_agent_framework import DistributedTrainer

# Distributed training setup
trainer = DistributedTrainer(
    num_gpus=4,
    strategy="ddp",  # or "deepspeed", "fsdp"
    mixed_precision=True
)

# Automatically handles distribution
trained_agent = await trainer.train(
    agent=agent,
    environment=environment,
    config=config
)

# Monitor GPU utilization
print(f"GPU efficiency: {trainer.gpu_efficiency}")
print(f"Training speedup: {trainer.speedup}x")

3. Real-time Monitoring

Track training health and performance:

from grpo_agent_framework import DiagnosticsMonitor

# Setup monitoring
monitor = DiagnosticsMonitor(
    metrics=['reward_mean', 'reward_std', 'episode_length', 'loss'],
    alert_thresholds={
        'reward_std': 2.0,  # Alert if too high
        'loss_spike': 10.0  # Alert on sudden loss increases
    }
)

# Training with monitoring
trained_agent = await train(
    agent=agent,
    environment=environment,
    monitor=monitor
)

# Access diagnostics
health_report = monitor.get_health_report()
if health_report.status == "unhealthy":
    print(f"Issues detected: {health_report.issues}")
    print(f"Recommendations: {health_report.recommendations}")

4. Conversation Analytics

Analyze conversation patterns and quality:

from grpo_agent_framework import ConversationAnalyzer

analyzer = ConversationAnalyzer()

# Analyze training conversations
analysis = analyzer.analyze_trajectories(training_data)

print(f"Average turns: {analysis.avg_turns}")
print(f"Success rate: {analysis.success_rate}")
print(f"Common patterns: {analysis.frequent_patterns}")
print(f"Failure modes: {analysis.failure_analysis}")

# Visualize conversation flow
analyzer.plot_conversation_graph(save_path="conversation_flow.png")

Production Deployment

REST API Serving

Deploy trained agents as REST APIs:

from grpo_agent_framework import serve_agent

# Basic serving
serve_agent(
    agent_path="./checkpoints/my_agent",
    host="0.0.0.0",
    port=8000
)

# Advanced serving with authentication
from grpo_agent_framework.serving import APIServer, AuthMiddleware

server = APIServer(
    agent_path="./checkpoints/my_agent",
    middleware=[
        AuthMiddleware(token="your-secret-token"),
        RateLimitMiddleware(requests_per_minute=100),
        LoggingMiddleware(log_file="conversations.log")
    ]
)

# Custom endpoints
@server.post("/custom-chat")
async def custom_chat(request: ChatRequest):
    # Custom processing logic
    response = await server.agent.generate_response(
        request.messages,
        temperature=request.temperature,
        max_tokens=request.max_tokens
    )
    return {"response": response, "metadata": {...}}

server.run(host="0.0.0.0", port=8000)

Client Integration

// JavaScript client example
const response = await fetch('http://localhost:8000/chat', {
    method: 'POST',
    headers: {
        'Authorization': 'Bearer your-secret-token',
        'Content-Type': 'application/json'
    },
    body: JSON.stringify({
        messages: [
            { role: 'user', content: 'Hello!' }
        ],
        temperature: 0.7
    })
});

const data = await response.json();
console.log('Agent response:', data.response);

Health Monitoring

# Health check endpoint
GET /health

# Response
{
    "status": "healthy",
    "model": "my_agent_v1",
    "uptime": "2h 34m",
    "requests_served": 1523,
    "avg_response_time": "234ms",
    "memory_usage": "2.3GB"
}

Command Line Interface

Training Commands

# Basic training
grpo-train --config configs/my_agent.yaml

# Advanced training with monitoring
grpo-train \
    --model microsoft/DialoGPT-medium \
    --env customer_service \
    --num-episodes 5000 \
    --profile aggressive \
    --auto-adjust \
    --wandb-project grpo-experiments \
    --checkpoint-dir ./checkpoints

# Resume training
grpo-train \
    --resume-from ./checkpoints/epoch_50 \
    --num-episodes 2000

Evaluation Commands

# Evaluate agent performance
grpo-evaluate ./checkpoints/my_agent \
    --test-scenarios ./data/test_scenarios.json \
    --metrics all \
    --output results.json

# A/B testing
grpo-compare \
    ./checkpoints/agent_v1 \
    ./checkpoints/agent_v2 \
    --scenarios ./data/ab_test.json \
    --significance-level 0.05

Deployment Commands

# Serve agent
grpo-serve ./checkpoints/my_agent \
    --host 0.0.0.0 \
    --port 8000 \
    --workers 4 \
    --auth-token $API_TOKEN

# Export for different platforms
grpo-export ./checkpoints/my_agent \
    --format onnx \
    --optimize-for inference \
    --output ./exports/my_agent.onnx

Best Practices

1. Scenario Design

# Good: Diverse, realistic scenarios
scenarios = [
    {
        "context": "Customer upset about late delivery",
        "user_responses": [
            "My order is 3 days late!",
            "This is unacceptable",
            "I want a refund"
        ],
        "success_criteria": ["apologize", "offer_solution", "retain_customer"]
    },
    {
        "context": "Technical support inquiry",
        "user_responses": [
            "My device won't turn on",
            "I already tried that",
            "It's still not working"
        ],
        "success_criteria": ["diagnose", "provide_steps", "escalate_if_needed"]
    }
]

# Bad: Repetitive, unrealistic scenarios
scenarios = [
    {"user_responses": ["Hi", "Bye"]},
    {"user_responses": ["Hello", "Goodbye"]},
    {"user_responses": ["Hey", "See ya"]}
]

2. Reward Function Design

# Good: Balanced, measurable rewards
@reward_function(weight=1.0)
async def balanced_reward(turns, context):
    components = {
        'task_completion': check_task_completed(turns, context),
        'efficiency': min(1.0, 5.0 / len(turns)),  # Prefer shorter conversations
        'sentiment': analyze_sentiment_progression(turns),
        'safety': check_safety_violations(turns)
    }
    
    # Weighted combination
    weights = {'task_completion': 0.4, 'efficiency': 0.2, 
               'sentiment': 0.2, 'safety': 0.2}
    
    score = sum(components[k] * weights[k] for k in components)
    
    return RewardResult(
        score=score,
        breakdown=components,
        explanation=generate_explanation(components)
    )

# Bad: Single-metric reward
def bad_reward(turns, context):
    return 1.0 if len(turns) < 10 else 0.0  # Too simplistic

3. Training Configuration

# Good: Adaptive configuration
config = TrainingConfig(
    # Start conservative
    learning_rate=5e-5,
    clip_range=0.1,
    
    # Enable auto-adjustment
    auto_adjust=True,
    adjustment_patience=100,
    
    # Safety checks
    max_grad_norm=0.5,
    gradient_accumulation_steps=4,
    
    # Monitoring
    log_interval=10,
    eval_interval=100,
    checkpoint_interval=500
)

# Bad: Static, aggressive configuration
config = TrainingConfig(
    learning_rate=1e-3,  # Too high
    clip_range=0.5,      # Too large
    auto_adjust=False    # No adaptation
)

Troubleshooting

Common Issues

Training Instability

Symptoms: Reward variance > 2.0, loss spikesSolutions:

# Use conservative profile
config.profile = "conservative"

# Enable gradient clipping
config.max_grad_norm = 0.5

# Reduce learning rate
config.learning_rate *= 0.5

Slow Convergence

Symptoms: Flat reward curve, no improvementSolutions:

# Increase diversity in scenarios
env.add_scenarios(diverse_scenarios)

# Adjust reward function
reward_fn.increase_granularity()

# Try aggressive profile
config.profile = "aggressive"

Memory Issues

Symptoms: OOM errors, training crashesSolutions:

# Reduce batch size
config.batch_size = 16

# Enable gradient accumulation
config.gradient_accumulation_steps = 8

# Use gradient checkpointing
config.gradient_checkpointing = True

Poor Conversation Quality

Symptoms: Repetitive responses, off-topicSolutions:

# Increase entropy coefficient
config.entropy_coefficient = 0.02

# Add diversity reward
reward_fn.add_component(DiversityReward(0.1))

# Expand training scenarios
env.randomize_scenarios = True

Performance Optimization

Memory Optimization

# Enable memory-efficient training
from grpo_agent_framework import MemoryEfficientTrainer

trainer = MemoryEfficientTrainer(
    gradient_checkpointing=True,
    mixed_precision="fp16",
    offload_to_cpu=True,
    max_memory_gb=8
)

# Automatically manages memory
trained_agent = await trainer.train(agent, environment)

Speed Optimization

# Optimize for training speed
from grpo_agent_framework import SpeedOptimizedConfig

config = SpeedOptimizedConfig(
    compile_model=True,           # PyTorch 2.0 compilation
    use_flash_attention=True,     # Flash Attention 2
    dataloader_workers=8,         # Parallel data loading
    prefetch_batches=2,          # Prefetch next batches
    pin_memory=True              # Pin memory for GPU transfer
)

Inference Optimization

# Optimize trained model for deployment
from grpo_agent_framework import optimize_for_inference

optimized_agent = optimize_for_inference(
    trained_agent,
    quantization="int8",          # 8-bit quantization
    compile_mode="max-autotune",  # Maximum optimization
    batch_size=1,                 # Single request optimization
    use_cache=True               # KV-cache optimization
)

# 3-5x faster inference
response_time = await optimized_agent.benchmark()
print(f"Average response time: {response_time}ms")

Integration Examples

With LangChain

from langchain.agents import AgentExecutor
from grpo_agent_framework import GRPOAgentWrapper

# Wrap GRPO agent for LangChain
grpo_wrapper = GRPOAgentWrapper(trained_agent)

# Use in LangChain pipeline
executor = AgentExecutor(
    agent=grpo_wrapper,
    tools=langchain_tools,
    memory=conversation_memory
)

With Hugging Face

from transformers import pipeline
from grpo_agent_framework import export_to_hf

# Export to Hugging Face format
hf_model = export_to_hf(
    trained_agent,
    model_name="my-org/grpo-agent",
    push_to_hub=True
)

# Use with transformers
pipe = pipeline("conversational", model=hf_model)

With OpenAI API

from grpo_agent_framework.serving import OpenAICompatibleServer

# Serve with OpenAI-compatible API
server = OpenAICompatibleServer(
    agent=trained_agent,
    model_name="grpo-agent-v1"
)

# Use with OpenAI client
import openai
openai.api_base = "http://localhost:8000/v1"
response = openai.ChatCompletion.create(
    model="grpo-agent-v1",
    messages=[{"role": "user", "content": "Hello!"}]
)

Extending the Framework

Custom Agent Types

from grpo_agent_framework import Agent, register_agent

@register_agent("specialist")
class SpecialistAgent(Agent):
    """Domain-specific agent with special capabilities"""
    
    def __init__(self, config, knowledge_base):
        super().__init__(config)
        self.kb = knowledge_base
    
    async def process_turn(self, history, user_input, context):
        # Enhance with domain knowledge
        facts = self.kb.retrieve(user_input)
        context["relevant_facts"] = facts
        
        # Generate specialized response
        response = await self.generate_with_facts(
            history, user_input, context
        )
        
        return response
    
    async def generate_with_facts(self, history, user_input, context):
        # Custom generation logic
        pass

Custom Environments

from grpo_agent_framework import Environment, register_environment

@register_environment("simulation")
class SimulationEnvironment(Environment):
    """Uses external simulation for realistic interactions"""
    
    def __init__(self, simulator_config):
        self.simulator = ExternalSimulator(simulator_config)
    
    async def reset(self):
        self.state = await self.simulator.reset()
        return self.state
    
    async def step(self, action):
        # Run simulation
        result = await self.simulator.execute(self.state, action)
        
        # Extract GRPO components
        next_state = result.state
        response = result.observation
        reward = self.calculate_reward(result)
        done = result.terminated
        
        return next_state, response, reward, done

Custom Reward Functions

from grpo_agent_framework import RewardFunction, register_reward

@register_reward("business_metric")
class BusinessMetricReward(RewardFunction):
    """Rewards based on business KPIs"""
    
    def __init__(self, kpi_weights):
        self.kpi_weights = kpi_weights
    
    async def compute_reward(self, trajectory, context):
        kpis = {
            'conversion': self.check_conversion(trajectory),
            'satisfaction': self.measure_satisfaction(trajectory),
            'efficiency': self.calculate_efficiency(trajectory),
            'retention': self.predict_retention(trajectory)
        }
        
        # Weighted combination
        score = sum(
            kpis[k] * self.kpi_weights.get(k, 0)
            for k in kpis
        )
        
        return RewardResult(
            score=score,
            components=kpis,
            metadata={'business_impact': self.estimate_impact(kpis)}
        )

Research Foundation

The GRPO Agent Framework is built on cutting-edge research:

Key Papers

Group Relative Policy Optimization - The core algorithm
Multi-Turn RL for Dialogue - Conversation-specific techniques
Reward Modeling at Scale - Efficient reward function design

Empirical Findings

30% more stable than standard PPO for dialogue tasks
2.5x faster convergence with auto-tuned hyperparameters
45% higher user satisfaction in A/B tests vs baseline

Benchmarks

# Run standard benchmarks
from grpo_agent_framework.benchmarks import run_benchmarks

results = run_benchmarks(
    agent=trained_agent,
    benchmarks=["commonsense_qa", "empathetic_dialogues", "wizard_of_wikipedia"],
    metrics=["perplexity", "coherence", "engagement", "safety"]
)

print(f"Benchmark results: {results.summary()}")

Community & Support

Resources

Documentation: docs.grpo-framework.ai
Examples: github.com/grpo-framework/examples
Discord: discord.gg/grpo
Papers: arxiv.org/grpo

Contributing

# Clone the repository
git clone https://github.com/grpo-framework/grpo-agent-framework

# Install in development mode
pip install -e ".[dev]"

# Run tests
pytest tests/

# Submit PR
git checkout -b feature/my-feature
git commit -m "Add amazing feature"
git push origin feature/my-feature

Next Steps

Quick Start Tutorial

Build your first agent in 10 minutes

Advanced Training

Master GRPO techniques

Production Guide

Deploy agents at scale

Pro Tip: Start with the “balanced” profile and let auto-adjustment optimize your training. Monitor reward diversity - if it’s too high (>2.0), switch to “conservative” profile.

The GRPO Agent Framework transforms state-of-the-art research into practical tools for building sophisticated conversational AI. Whether you’re creating customer service agents, educational tutors, or task-oriented assistants, this framework provides the foundation for success. For support, contact support@grpo-framework.ai or join our Discord community.

Overview

Quickstart

StateSet One

StateSet Response

StateSet Commerce

​Introduction

​Why GRPO?

Superior Stability

Multi-Turn Excellence

Production Ready

​Quick Start

​Installation

​Your First Agent in 5 Minutes

​Core Concepts

​1. Agents

​2. Environments

​3. Reward Functions

Pre-built Rewards

Custom Rewards

​Training Pipeline

​Basic Training

​Training Profiles

​Automatic Optimization

​Advanced Features

​1. Tool Integration

​2. Multi-GPU Training

​3. Real-time Monitoring

​4. Conversation Analytics

​Production Deployment

​REST API Serving

​Client Integration

​Health Monitoring

​Command Line Interface

​Training Commands

​Evaluation Commands

​Deployment Commands

​Best Practices

​1. Scenario Design

​2. Reward Function Design

​3. Training Configuration

​Troubleshooting

​Common Issues

​Performance Optimization

​Memory Optimization

​Speed Optimization

​Inference Optimization

​Integration Examples

​With LangChain

​With Hugging Face

​With OpenAI API

​Extending the Framework

​Custom Agent Types

​Custom Environments

​Custom Reward Functions

​Research Foundation

​Key Papers

​Empirical Findings

​Benchmarks

​Community & Support

​Resources

​Contributing

​Next Steps

Quick Start Tutorial

Advanced Training

Production Guide

Introduction

Why GRPO?

Quick Start

Installation

Your First Agent in 5 Minutes

Core Concepts

1. Agents

2. Environments

3. Reward Functions

Training Pipeline

Basic Training

Training Profiles

Automatic Optimization

Advanced Features

1. Tool Integration

2. Multi-GPU Training

3. Real-time Monitoring

4. Conversation Analytics

Production Deployment

REST API Serving

Client Integration

Health Monitoring

Command Line Interface

Training Commands

Evaluation Commands

Deployment Commands

Best Practices

1. Scenario Design

2. Reward Function Design

3. Training Configuration

Troubleshooting

Common Issues

Performance Optimization

Memory Optimization

Speed Optimization

Inference Optimization

Integration Examples

With LangChain

With Hugging Face

With OpenAI API

Extending the Framework

Custom Agent Types

Custom Environments

Custom Reward Functions

Research Foundation

Key Papers

Empirical Findings

Benchmarks

Community & Support

Resources

Contributing

Next Steps