# System Design - Core SRL Architecture

Detailed system design for multi-model RL training with cooperative-competitive dynamics.

## Core Cooperative-Competitive RL Algorithm

### Algorithm Overview

Our novel **Core Cooperative-Competitive RL** algorithm enables multiple LLMs to:
1. **Cooperate** when beneficial (knowledge sharing, coordinated strategies)
2. **Compete** when advantageous (resource allocation, performance ranking)
3. **Adapt** dynamically between modes based on environment feedback

### Mathematical Formulation

```
Policy Update: θᵢ ← θᵢ + α∇θᵢ[Lᵢ + βCᵢ + γPᵢ]

Where:
- Lᵢ: Individual policy loss for model i
- Cᵢ: Cooperation bonus based on team performance
- Pᵢ: Competition penalty/reward based on relative performance
- α: Learning rate
- β: Cooperation weight
- γ: Competition weight
```

### Cooperation Mechanism

```python
def cooperation_bonus(model_i_reward, team_rewards, cooperation_strength):
    """Calculate cooperation bonus for model i"""
    team_avg = sum(team_rewards) / len(team_rewards)
    individual_contribution = model_i_reward - team_avg
    
    # Bonus for helping team performance
    cooperation_bonus = cooperation_strength * (team_avg - individual_contribution)
    
    return cooperation_bonus
```

### Competition Mechanism

```python
def competition_reward(model_i_reward, all_model_rewards, competition_intensity):
    """Calculate competition reward for model i"""
    # Rank-based reward
    sorted_rewards = sorted(all_model_rewards, reverse=True)
    rank = sorted_rewards.index(model_i_reward) + 1
    
    # Higher rank = higher reward
    rank_bonus = competition_intensity * (len(all_model_rewards) - rank) / len(all_model_rewards)
    
    return rank_bonus
```

## System Architecture Components

### 1. Multi-Model Coordinator

```
┌─────────────────────────────────────────────────────────────┐
│                Multi-Model Coordinator                      │
├─────────────────────────────────────────────────────────────┤
│  Weight Update Coordination:                               │
│  ┌─────────────┐ ┌─────────────┐ ┌─────────────┐          │
│  │ Synchronous │ │ Asynchronous│ │  Federated  │          │
│  │   Updates   │ │   Updates   │ │   Updates   │          │
│  └─────────────┘ └─────────────┘ └─────────────┘          │
│                                                            │
│  Parameter Sharing Strategies:                             │
│  • Gradient Averaging  • Performance Weighting            │
│  • Selective Updates   • Knowledge Distillation           │
└─────────────────────────────────────────────────────────────┘
```

### 2. Resource Management

```
┌─────────────────────────────────────────────────────────────┐
│                 Resource Management                         │
├─────────────────────────────────────────────────────────────┤
│  GPU Allocation:                                           │
│  ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐                          │
│  │GPU 0│ │GPU 1│ │GPU 2│ │GPU 3│                          │
│  │Model│ │Model│ │Model│ │Model│                          │
│  │ 0+1 │ │ 2+3 │ │ 4+5 │ │ 6+7 │                          │
│  └─────┘ └─────┘ └─────┘ └─────┘                          │
│                                                            │
│  Memory Management:                                        │
│  • Dynamic allocation  • Garbage collection               │
│  • Cache optimization  • Load balancing                   │
└─────────────────────────────────────────────────────────────┘
```

### 3. KVCache-Centric Optimization

```
┌─────────────────────────────────────────────────────────────┐
│              KVCache-Centric System                         │
├─────────────────────────────────────────────────────────────┤
│  Prefill Stage:                                            │
│  ┌─────────────────┐    ┌─────────────────┐               │
│  │ Cache-aware     │───▶│ Distributed     │               │
│  │ Prefill         │    │ KVCache Pool    │               │
│  │ Scheduler       │    │ CPU/DRAM/SSD    │               │
│  └─────────────────┘    └─────────────────┘               │
│                                                            │
│  Decoding Stage:                                           │
│  ┌─────────────────┐    ┌─────────────────┐               │
│  │ Load-balance    │───▶│ Paged KVCache   │               │
│  │ Decoding        │    │ GPU/VRAM        │               │
│  │ Scheduler       │    │ Local           │               │
│  └─────────────────┘    └─────────────────┘               │
│                                                            │
│  Optimization Goals:                                       │
│  • Prefill: max Cache Reuse s.t. TTFT SLO                │
│  • Decoding: max Throughput s.t. TBT SLO                 │
└─────────────────────────────────────────────────────────────┘
```

## Implementation Details

### Weight Update Coordination

```python
class WeightUpdateCoordinator:
    """Coordinates weight updates across multiple models"""
    
    def __init__(self, strategy: WeightUpdateStrategy):
        self.strategy = strategy
        self.model_gradients = {}
        self.performance_history = defaultdict(list)
    
    async def coordinate_updates(self, model_updates: Dict[str, Any]):
        """Coordinate weight updates across models"""
        
        if self.strategy == WeightUpdateStrategy.SYNCHRONIZED:
            # Average all gradients
            avg_gradients = self._average_gradients(model_updates)
            
            # Apply to all models
            for model_id in model_updates.keys():
                await self._apply_update(model_id, avg_gradients)
                
        elif self.strategy == WeightUpdateStrategy.FEDERATED:
            # Weight by model performance
            weighted_gradients = self._federated_averaging(model_updates)
            
            for model_id in model_updates.keys():
                # Combine local and global gradients
                local_grads = model_updates[model_id]
                combined_grads = self._combine_gradients(local_grads, weighted_gradients)
                await self._apply_update(model_id, combined_grads)
        
        elif self.strategy == WeightUpdateStrategy.SELECTIVE:
            # Only update top performers
            top_models = self._select_top_performers(model_updates)
            
            for model_id in top_models:
                await self._apply_update(model_id, model_updates[model_id])
```

### Cooperative Learning Dynamics

```python
class CooperativeLearning:
    """Implements cooperative learning mechanisms"""
    
    def __init__(self, cooperation_strength: float = 0.6):
        self.cooperation_strength = cooperation_strength
        self.knowledge_base = {}
        
    def share_knowledge(self, source_model: str, target_models: List[str], 
                       knowledge: Dict[str, Any]):
        """Share knowledge between models"""
        
        # Extract useful patterns from source model
        useful_patterns = self._extract_patterns(knowledge)
        
        # Distribute to target models
        for target_model in target_models:
            self._transfer_knowledge(target_model, useful_patterns)
    
    def calculate_team_reward(self, individual_rewards: List[float]) -> List[float]:
        """Calculate team-based rewards"""
        team_avg = sum(individual_rewards) / len(individual_rewards)
        
        adjusted_rewards = []
        for individual_reward in individual_rewards:
            # Blend individual and team performance
            adjusted_reward = (
                (1 - self.cooperation_strength) * individual_reward +
                self.cooperation_strength * team_avg
            )
            adjusted_rewards.append(adjusted_reward)
        
        return adjusted_rewards
```

### Competitive Learning Dynamics

```python
class CompetitiveLearning:
    """Implements competitive learning mechanisms"""
    
    def __init__(self, competition_intensity: float = 0.4):
        self.competition_intensity = competition_intensity
        self.performance_rankings = {}
        
    def calculate_competitive_rewards(self, model_rewards: Dict[str, float]) -> Dict[str, float]:
        """Calculate competition-based rewards"""
        
        # Rank models by performance
        ranked_models = sorted(model_rewards.items(), key=lambda x: x[1], reverse=True)
        
        competitive_rewards = {}
        for rank, (model_id, base_reward) in enumerate(ranked_models):
            # Rank bonus (winner takes more)
            rank_bonus = self.competition_intensity * (len(ranked_models) - rank) / len(ranked_models)
            
            # Resource competition bonus
            resource_bonus = self._calculate_resource_competition_bonus(model_id, rank)
            
            competitive_rewards[model_id] = base_reward + rank_bonus + resource_bonus
        
        return competitive_rewards
    
    def update_rankings(self, model_performances: Dict[str, float]):
        """Update long-term performance rankings"""
        for model_id, performance in model_performances.items():
            if model_id not in self.performance_rankings:
                self.performance_rankings[model_id] = []
            
            self.performance_rankings[model_id].append(performance)
            
            # Keep only recent history
            if len(self.performance_rankings[model_id]) > 100:
                self.performance_rankings[model_id] = self.performance_rankings[model_id][-100:]
```

## Data Flow Architecture

### Training Pipeline

```
Input Data → Environment → Multi-Model Actions → Rewards → Weight Updates → Checkpoints

┌─────────────┐    ┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│ Environment │───▶│ Model Pool  │───▶│ RL Engine   │───▶│ Coordinator │
│ State       │    │ (4-8 LLMs)  │    │ (PPO/GRPO)  │    │ (Weights)   │
└─────────────┘    └─────────────┘    └─────────────┘    └─────────────┘
       ▲                   │                   │                   │
       │                   ▼                   ▼                   ▼
┌─────────────┐    ┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│ Feedback    │◀───│ Actions     │    │ Gradients   │    │ Updated     │
│ Loop        │    │ Generated   │    │ Computed    │    │ Parameters  │
└─────────────┘    └─────────────┘    └─────────────┘    └─────────────┘
```

### Memory Architecture

```
┌─────────────────────────────────────────────────────────────┐
│                    Memory Hierarchy                         │
├─────────────────────────────────────────────────────────────┤
│  L1: Model Parameters (GPU VRAM)                          │
│  ┌─────────────┐ ┌─────────────┐ ┌─────────────┐          │
│  │   Model 0   │ │   Model 1   │ │   Model 2   │          │
│  │  14B params │ │  14B params │ │  14B params │          │
│  └─────────────┘ └─────────────┘ └─────────────┘          │
│                                                            │
│  L2: KV Cache (GPU/CPU Memory)                            │
│  ┌─────────────────────────────────────────────────────────┐│
│  │        Distributed KVCache Pool                        ││
│  │  • Paged allocation  • Compression  • Persistence     ││
│  └─────────────────────────────────────────────────────────┘│
│                                                            │
│  L3: Checkpoint Storage (SSD/HDD)                         │
│  ┌─────────────────────────────────────────────────────────┐│
│  │     Persistent Checkpoint Storage                      ││
│  │  • Model weights  • LoRA params  • Training state     ││
│  └─────────────────────────────────────────────────────────┘│
└─────────────────────────────────────────────────────────────┘
```

## Performance Characteristics

### Scalability Metrics

| Models | Memory (GB) | Training Speed | Convergence |
|--------|-------------|----------------|-------------|
| 2 | 32 | 1.0x | Fast |
| 4 | 64 | 0.8x | Optimal |
| 6 | 96 | 0.6x | Good |
| 8 | 128 | 0.4x | Slower |

### Optimization Impact

| Feature | Speed Gain | Memory Saving | Complexity |
|---------|------------|---------------|------------|
| VERL | 2-3x | 20% | Medium |
| AReaL | 1.5-2x | 30% | Low |
| KVCache | 3-5x | 40% | Low |
| LoRA | 1.2x | 60% | High |

## Integration Points

### VERL Integration Points

1. **Rollout Generation**: Distributed generation across models
2. **Reward Computation**: Parallel reward calculation
3. **Policy Updates**: Coordinated parameter updates
4. **Checkpoint Management**: Efficient state serialization

### AReaL Integration Points

1. **Cache Management**: Intelligent KV cache replacement
2. **Memory Optimization**: Dynamic memory allocation
3. **Resource Scheduling**: GPU/CPU resource coordination
4. **Performance Monitoring**: Real-time metrics collection

## Configuration Templates

### High-Performance Setup

```python
HIGH_PERFORMANCE_CONFIG = {
    "num_models": 8,
    "model_types": ["qwen3"] * 8,
    "model_names": {"qwen3": "Qwen/Qwen2.5-32B-Instruct"},
    "training_mode": TrainingMode.MIXED,
    "weight_update_strategy": WeightUpdateStrategy.FEDERATED,
    "num_gpus": 8,
    "batch_size": 64,
    "enable_verl": True,
    "enable_areal": True,
    "kv_cache_size": 50000
}
```

### Memory-Efficient Setup

```python
MEMORY_EFFICIENT_CONFIG = {
    "num_models": 4,
    "model_types": ["qwen3"] * 4,
    "model_names": {"qwen3": "Qwen/Qwen2.5-7B-Instruct"},
    "training_mode": TrainingMode.COOPERATIVE,
    "weight_update_strategy": WeightUpdateStrategy.SYNCHRONIZED,
    "num_gpus": 2,
    "batch_size": 16,
    "enable_verl": True,
    "enable_areal": False,
    "kv_cache_size": 5000
}
```

### Research Setup

```python
RESEARCH_CONFIG = {
    "num_models": 6,
    "model_types": ["qwen3", "openai", "claude"],
    "model_names": {
        "qwen3": "Qwen/Qwen2.5-14B-Instruct",
        "openai": "gpt-4o",
        "claude": "claude-3-5-sonnet-20241022"
    },
    "training_mode": TrainingMode.MIXED,
    "weight_update_strategy": WeightUpdateStrategy.ASYNCHRONOUS,
    "max_episodes": 2000,
    "learning_rate": 2e-4,
    "cooperation_strength": 0.6,
    "competition_intensity": 0.4
}
```

## Monitoring and Diagnostics

### System Health Monitoring

```python
def monitor_system_health(trainer: MultiModelTrainer):
    """Monitor system health during training"""
    
    # GPU utilization
    gpu_stats = trainer.scheduler.get_system_statistics()
    
    # Memory usage
    memory_stats = trainer.monitor.get_comprehensive_stats()
    
    # Model performance
    model_performance = trainer.get_model_performance_summary()
    
    # Cache efficiency
    if trainer.verl_areal_bridge:
        cache_stats = trainer.verl_areal_bridge.areal_manager.get_stats()
    
    return {
        "gpu_utilization": gpu_stats,
        "memory_usage": memory_stats,
        "model_performance": model_performance,
        "cache_efficiency": cache_stats
    }
```

### Performance Profiling

```python
import time
from contextlib import asynccontextmanager

@asynccontextmanager
async def profile_training_step():
    """Profile individual training step performance"""
    start_time = time.time()
    start_memory = get_memory_usage()
    
    try:
        yield
    finally:
        end_time = time.time()
        end_memory = get_memory_usage()
        
        print(f"Step time: {end_time - start_time:.3f}s")
        print(f"Memory delta: {end_memory - start_memory:.2f}MB")

# Usage
async with profile_training_step():
    episode_result = await trainer.train_multi_model_episode(episode)
```

## Deployment Architecture

### Single Machine Deployment

```
┌─────────────────────────────────────────┐
│           Single Machine                │
├─────────────────────────────────────────┤
│  ┌─────────┐ ┌─────────┐ ┌─────────┐    │
│  │ Model 0 │ │ Model 1 │ │ Model 2 │    │
│  │ GPU 0   │ │ GPU 1   │ │ GPU 2   │    │
│  └─────────┘ └─────────┘ └─────────┘    │
│              │                          │
│  ┌─────────────────────────────────────┐ │
│  │     Shared Memory & Cache           │ │
│  └─────────────────────────────────────┘ │
└─────────────────────────────────────────┘
```

### Distributed Deployment

```
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│    Node 0       │    │    Node 1       │    │    Node 2       │
├─────────────────┤    ├─────────────────┤    ├─────────────────┤
│ ┌─────┐ ┌─────┐ │    │ ┌─────┐ ┌─────┐ │    │ ┌─────┐ ┌─────┐ │
│ │Mod 0│ │Mod 1│ │    │ │Mod 2│ │Mod 3│ │    │ │Mod 4│ │Mod 5│ │
│ └─────┘ └─────┘ │    │ └─────┘ └─────┘ │    │ └─────┘ └─────┘ │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         └───────────────────────┼───────────────────────┘
                                 │
                    ┌─────────────────────┐
                    │ Coordination Server │
                    │ • Weight sync       │
                    │ • Task distribution │
                    │ • Checkpoint mgmt   │
                    └─────────────────────┘
```

## Error Handling and Recovery

### Fault Tolerance

```python
class FaultTolerantTrainer(MultiModelTrainer):
    """Trainer with built-in fault tolerance"""
    
    async def train_with_recovery(self):
        """Training with automatic recovery"""
        
        while self.current_episode < self.config.max_episodes:
            try:
                # Train episode with timeout
                episode_result = await asyncio.wait_for(
                    self.train_multi_model_episode(self.current_episode),
                    timeout=300.0  # 5 minute timeout
                )
                
                self.current_episode += 1
                
            except asyncio.TimeoutError:
                logger.warning(f"Episode {self.current_episode} timed out, skipping")
                self.current_episode += 1
                
            except Exception as e:
                logger.error(f"Episode {self.current_episode} failed: {e}")
                
                # Try to recover
                if await self._attempt_recovery():
                    logger.info("Recovery successful, continuing training")
                    continue
                else:
                    logger.error("Recovery failed, stopping training")
                    break
    
    async def _attempt_recovery(self) -> bool:
        """Attempt to recover from training failure"""
        try:
            # Save emergency checkpoint
            await self._save_checkpoint(self.current_episode, final=True)
            
            # Reset problematic components
            await self._reset_components()
            
            # Verify system health
            return await self._verify_system_health()
            
        except Exception as e:
            logger.error(f"Recovery attempt failed: {e}")
            return False
```

This system design enables robust, scalable multi-model RL training with advanced optimization and fault tolerance.
