Reinforcement Learning from Human Feedback in Production Agent Systems

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259

import torch
import torch.nn as nn
from typing import Dict, List, Tuple, Optional
from dataclasses import dataclass

@dataclass
class AgentObservation:
    """Structured observation for a single agent"""
    state_vector: torch.Tensor
    action_history: torch.Tensor
    communication_context: torch.Tensor
    task_progress: float

@dataclass
class SystemState:
    """Global system state for reward computation"""
    agent_observations: List[AgentObservation]
    environment_state: torch.Tensor
    coordination_graph: torch.Tensor
    safety_metrics: Dict[str, float]

class MultiAgentRewardModel(nn.Module):
    """Production reward model for multi-agent systems"""
    
    def __init__(self, config: Dict):
        super().__init__()
        self.config = config
        
        # Individual agent value networks
        self.agent_value_net = nn.ModuleDict({
            agent_type: self._build_agent_network(config[agent_type])
            for agent_type in config['agent_types']
        })
        
        # Coordination quality network
        self.coordination_net = nn.Sequential(
            nn.Linear(config['coordination_dim'], 256),
            nn.ReLU(),
            nn.Dropout(0.1),
            nn.Linear(256, 128),
            nn.ReLU(),
            nn.Linear(128, 1)
        )
        
        # Safety constraint network
        self.safety_net = nn.Sequential(
            nn.Linear(config['safety_dim'], 128),
            nn.ReLU(),
            nn.Linear(128, 64),
            nn.ReLU(),
            nn.Linear(64, config['num_safety_constraints'])
        )
        
        # Global value aggregation
        self.global_aggregator = nn.MultiheadAttention(
            embed_dim=config['global_dim'],
            num_heads=8,
            dropout=0.1
        )
    
    def _build_agent_network(self, agent_config: Dict) -> nn.Module:
        """Build value network for specific agent type"""
        layers = []
        input_dim = agent_config['state_dim']
        
        for hidden_dim in agent_config['hidden_dims']:
            layers.extend([
                nn.Linear(input_dim, hidden_dim),
                nn.ReLU(),
                nn.Dropout(0.1)
            ])
            input_dim = hidden_dim
        
        layers.append(nn.Linear(input_dim, 1))
        return nn.Sequential(*layers)
    
    def forward(self, system_state: SystemState) -> Dict[str, torch.Tensor]:
        """Compute multi-faceted reward signals"""
        batch_size = len(system_state.agent_observations)
        
        # Individual agent rewards
        agent_rewards = {}
        agent_features = []
        
        for i, obs in enumerate(system_state.agent_observations):
            agent_type = self.config['agent_types'][i % len(self.config['agent_types'])]
            
            # Agent-specific reward
            agent_reward = self.agent_value_net[agent_type](obs.state_vector)
            agent_rewards[f'agent_{i}'] = agent_reward
            
            # Collect features for global aggregation
            agent_features.append(obs.state_vector)
        
        # Coordination reward
        coordination_features = system_state.coordination_graph.flatten(start_dim=1)
        coordination_reward = self.coordination_net(coordination_features)
        
        # Safety constraint evaluation
        safety_features = torch.tensor([
            list(system_state.safety_metrics.values())
        ], dtype=torch.float32)
        safety_scores = self.safety_net(safety_features)
        
        # Global reward via attention mechanism
        if agent_features:
            agent_stack = torch.stack(agent_features, dim=1)  # [batch, num_agents, features]
            global_features, _ = self.global_aggregator(
                agent_stack, agent_stack, agent_stack
            )
            global_reward = torch.mean(global_features, dim=1).sum(dim=1, keepdim=True)
        else:
            global_reward = torch.zeros(batch_size, 1)
        
        return {
            'agent_rewards': agent_rewards,
            'coordination_reward': coordination_reward,
            'safety_scores': safety_scores,
            'global_reward': global_reward
        }

class ProductionRLHFTrainer:
    """RLHF trainer optimized for production agent systems"""
    
    def __init__(self, reward_model: MultiAgentRewardModel, config: Dict):
        self.reward_model = reward_model
        self.config = config
        self.preference_buffer = PreferenceBuffer(config['buffer_size'])
        self.reward_optimizer = torch.optim.AdamW(
            reward_model.parameters(),
            lr=config['reward_lr'],
            weight_decay=1e-5
        )
        
    def collect_human_feedback(self, 
                              trajectories: List[Trajectory], 
                              feedback_sources: List[str]) -> List[Preference]:
        """Collect and structure human feedback from multiple sources"""
        preferences = []
        
        for trajectory in trajectories:
            # Direct human ratings (when available)
            if 'human_rater' in feedback_sources:
                rating = self._get_human_rating(trajectory)
                if rating is not None:
                    preferences.append(
                        Preference(
                            trajectory=trajectory,
                            rating=rating,
                            source='human_rater',
                            confidence=1.0
                        )
                    )
            
            # Implicit behavioral signals
            if 'behavioral' in feedback_sources:
                behavioral_score = self._extract_behavioral_signals(trajectory)
                preferences.append(
                    Preference(
                        trajectory=trajectory,
                        rating=behavioral_score,
                        source='behavioral',
                        confidence=0.7
                    )
                )
            
            # Safety constraint violations
            if 'safety' in feedback_sources:
                safety_violations = self._check_safety_violations(trajectory)
                if safety_violations:
                    preferences.append(
                        Preference(
                            trajectory=trajectory,
                            rating=-1.0,  # Strong negative signal
                            source='safety',
                            confidence=1.0
                        )
                    )
        
        return preferences
    
    def update_reward_model(self, preferences: List[Preference]) -> Dict[str, float]:
        """Update reward model based on collected preferences"""
        self.preference_buffer.extend(preferences)
        
        if len(self.preference_buffer) < self.config['min_buffer_size']:
            return {'reward_loss': 0.0, 'accuracy': 0.0}
        
        batch = self.preference_buffer.sample(self.config['batch_size'])
        total_loss = 0.0
        correct_predictions = 0
        
        self.reward_optimizer.zero_grad()
        
        for preference_pair in batch:
            traj_a, traj_b, preference = preference_pair
            
            # Get reward predictions
            rewards_a = self.reward_model(traj_a.system_state)
            rewards_b = self.reward_model(traj_b.system_state)
            
            # Compute total reward (weighted sum of components)
            total_reward_a = self._compute_total_reward(rewards_a)
            total_reward_b = self._compute_total_reward(rewards_b)
            
            # Preference learning loss (Bradley-Terry model)
            preference_prob = torch.sigmoid(total_reward_a - total_reward_b)
            
            if preference > 0:  # A preferred over B
                loss = -torch.log(preference_prob + 1e-8)
                correct_predictions += (total_reward_a > total_reward_b).float()
            else:  # B preferred over A
                loss = -torch.log(1 - preference_prob + 1e-8)
                correct_predictions += (total_reward_b > total_reward_a).float()
            
            total_loss += loss
        
        total_loss /= len(batch)
        total_loss.backward()
        
        # Gradient clipping for stability
        torch.nn.utils.clip_grad_norm_(
            self.reward_model.parameters(), 
            self.config['grad_clip']
        )
        
        self.reward_optimizer.step()
        
        accuracy = correct_predictions / len(batch)
        
        return {
            'reward_loss': total_loss.item(),
            'accuracy': accuracy.item()
        }
    
    def _compute_total_reward(self, reward_dict: Dict[str, torch.Tensor]) -> torch.Tensor:
        """Compute weighted total reward from components"""
        weights = self.config['reward_weights']
        total_reward = 0.0
        
        # Individual agent rewards
        for key, reward in reward_dict['agent_rewards'].items():
            total_reward += weights['agent'] * reward
        
        # Coordination reward
        total_reward += weights['coordination'] * reward_dict['coordination_reward']
        
        # Safety penalty
        safety_penalty = torch.sum(
            torch.clamp(reward_dict['safety_scores'], max=0.0), 
            dim=1, 
            keepdim=True
        )
        total_reward += weights['safety'] * safety_penalty
        
        # Global reward
        total_reward += weights['global'] * reward_dict['global_reward']
        
        return total_reward

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58

class OnlineFeedbackCollector:
    """Collects feedback during live agent execution"""
    
    def __init__(self, config: Dict):
        self.config = config
        self.feedback_queue = asyncio.Queue()
        self.implicit_signals = ImplicitSignalExtractor()
        
    async def collect_realtime_feedback(self, 
                                       agent_session: AgentSession) -> None:
        """Collect feedback during agent execution"""
        
        # Set up feedback collection tasks
        tasks = [
            self._monitor_user_interactions(agent_session),
            self._track_task_completion(agent_session),
            self._detect_safety_violations(agent_session),
            self._measure_efficiency_metrics(agent_session)
        ]
        
        await asyncio.gather(*tasks)
    
    async def _monitor_user_interactions(self, session: AgentSession):
        """Monitor user behavior for implicit feedback signals"""
        async for interaction in session.user_interactions():
            # User correction signals
            if interaction.type == 'correction':
                feedback = NegativeFeedback(
                    trajectory_id=session.current_trajectory,
                    correction_type=interaction.correction_type,
                    timestamp=interaction.timestamp
                )
                await self.feedback_queue.put(feedback)
            
            # User confirmation signals
            elif interaction.type == 'confirmation':
                feedback = PositiveFeedback(
                    trajectory_id=session.current_trajectory,
                    confirmation_strength=interaction.strength,
                    timestamp=interaction.timestamp
                )
                await self.feedback_queue.put(feedback)
    
    async def _track_task_completion(self, session: AgentSession):
        """Track task completion quality"""
        async for task_result in session.task_completions():
            completion_quality = self.implicit_signals.evaluate_completion(
                task=task_result.task,
                result=task_result.result,
                user_reaction=task_result.user_reaction
            )
            
            feedback = TaskCompletionFeedback(
                trajectory_id=session.current_trajectory,
                quality_score=completion_quality,
                task_type=task_result.task.type
            )
            await self.feedback_queue.put(feedback)

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119

class SafePolicyUpdater:
    """Implements safe policy updates for production agents"""
    
    def __init__(self, base_policy: AgentPolicy, config: Dict):
        self.base_policy = base_policy
        self.config = config
        self.safety_checker = SafetyChecker(config['safety_config'])
        self.update_history = UpdateHistory()
        
    def update_policy(self, 
                     reward_model: MultiAgentRewardModel,
                     trajectories: List[Trajectory]) -> PolicyUpdateResult:
        """Safely update policy using PPO with safety constraints"""
        
        # Compute advantage estimates
        advantages = self._compute_gae_advantages(trajectories, reward_model)
        
        # Prepare training data
        states, actions, old_log_probs, returns = self._prepare_training_data(
            trajectories, advantages
        )
        
        update_result = PolicyUpdateResult()
        
        # PPO training loop with safety constraints
        for epoch in range(self.config['ppo_epochs']):
            for batch in self._create_batches(states, actions, old_log_probs, returns):
                
                # Compute current policy outputs
                new_log_probs, values, entropy = self.base_policy.evaluate_actions(
                    batch.states, batch.actions
                )
                
                # PPO loss computation
                ratio = torch.exp(new_log_probs - batch.old_log_probs)
                surrogate1 = ratio * batch.advantages
                surrogate2 = torch.clamp(
                    ratio, 
                    1.0 - self.config['ppo_epsilon'],
                    1.0 + self.config['ppo_epsilon']
                ) * batch.advantages
                
                policy_loss = -torch.min(surrogate1, surrogate2).mean()
                value_loss = nn.MSELoss()(values, batch.returns)
                entropy_loss = -entropy.mean()
                
                total_loss = (
                    policy_loss + 
                    self.config['value_coef'] * value_loss +
                    self.config['entropy_coef'] * entropy_loss
                )
                
                # Safety constraint checking
                safety_violation = self.safety_checker.check_policy_update(
                    old_policy=self.base_policy,
                    new_policy_gradients=torch.autograd.grad(
                        total_loss, self.base_policy.parameters(), retain_graph=True
                    )
                )
                
                if safety_violation.is_safe:
                    # Apply update
                    self.base_policy.optimizer.zero_grad()
                    total_loss.backward()
                    
                    # Gradient clipping
                    torch.nn.utils.clip_grad_norm_(
                        self.base_policy.parameters(),
                        self.config['grad_clip']
                    )
                    
                    self.base_policy.optimizer.step()
                    
                    update_result.successful_updates += 1
                else:
                    # Skip unsafe update
                    update_result.safety_rejections += 1
                    update_result.rejection_reasons.append(safety_violation.reason)
        
        # Update history tracking
        self.update_history.record_update(update_result)
        
        return update_result
    
    def _compute_gae_advantages(self, 
                               trajectories: List[Trajectory],
                               reward_model: MultiAgentRewardModel) -> torch.Tensor:
        """Compute Generalized Advantage Estimation"""
        advantages = []
        
        for trajectory in trajectories:
            rewards = []
            values = []
            
            for step in trajectory.steps:
                reward_dict = reward_model(step.system_state)
                total_reward = self._compute_total_reward(reward_dict)
                rewards.append(total_reward)
                
                value = self.base_policy.value_function(step.system_state)
                values.append(value)
            
            # GAE computation
            gae = 0
            traj_advantages = []
            
            for t in reversed(range(len(rewards))):
                if t == len(rewards) - 1:
                    next_value = 0
                else:
                    next_value = values[t + 1]
                
                delta = rewards[t] + self.config['gamma'] * next_value - values[t]
                gae = delta + self.config['gamma'] * self.config['lambda'] * gae
                traj_advantages.insert(0, gae)
            
            advantages.extend(traj_advantages)
        
        return torch.tensor(advantages, dtype=torch.float32)

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140

class ProductionRLHFPipeline:
    """Complete RLHF pipeline for production deployment"""
    
    def __init__(self, config: Dict):
        self.config = config
        
        # Core components
        self.reward_model = MultiAgentRewardModel(config['reward_model'])
        self.policy_updater = SafePolicyUpdater(
            base_policy=AgentPolicy.load(config['base_policy_path']),
            config=config['policy_update']
        )
        self.feedback_collector = OnlineFeedbackCollector(config['feedback'])
        
        # Infrastructure components
        self.experience_buffer = ExperienceBuffer(
            redis_url=config['redis_url'],
            max_size=config['buffer_size']
        )
        self.model_registry = ModelRegistry(config['registry'])
        self.metrics_tracker = MetricsTracker(config['monitoring'])
        
        # Training coordination
        self.training_scheduler = TrainingScheduler(config['scheduling'])
        
    async def run_continuous_learning(self):
        """Main continuous learning loop"""
        
        while True:
            try:
                # Collect recent experiences
                experiences = await self.experience_buffer.sample_recent(
                    self.config['training_batch_size']
                )
                
                if len(experiences) < self.config['min_training_size']:
                    await asyncio.sleep(self.config['collection_interval'])
                    continue
                
                # Process feedback and update reward model
                preferences = self._extract_preferences(experiences)
                reward_metrics = self.policy_updater.update_reward_model(preferences)
                
                # Update policy if enough high-quality feedback
                if self._should_update_policy(reward_metrics):
                    trajectories = self._experiences_to_trajectories(experiences)
                    policy_metrics = self.policy_updater.update_policy(
                        self.reward_model, trajectories
                    )
                    
                    # Validate updated policy
                    if self._validate_policy_update(policy_metrics):
                        # Deploy to subset of agents for A/B testing
                        await self._deploy_policy_update()
                    
                # Track metrics
                self.metrics_tracker.record_training_cycle({
                    'reward_metrics': reward_metrics,
                    'policy_metrics': policy_metrics if 'policy_metrics' in locals() else {},
                    'experience_count': len(experiences),
                    'timestamp': time.time()
                })
                
                # Adaptive sleep based on system load
                await asyncio.sleep(
                    self.training_scheduler.get_next_interval()
                )
                
            except Exception as e:
                logger.error(f"Training cycle error: {e}")
                await asyncio.sleep(self.config['error_retry_interval'])
    
    def _should_update_policy(self, reward_metrics: Dict[str, float]) -> bool:
        """Determine if policy should be updated based on reward model quality"""
        return (
            reward_metrics['accuracy'] > self.config['min_reward_accuracy'] and
            reward_metrics['reward_loss'] < self.config['max_reward_loss'] and
            len(self.experience_buffer) > self.config['min_experiences_for_update']
        )
    
    async def _deploy_policy_update(self):
        """Deploy policy update using gradual rollout"""
        
        # Save new policy version
        policy_version = await self.model_registry.save_policy(
            self.policy_updater.base_policy,
            metadata={'training_cycle': self.training_scheduler.current_cycle}
        )
        
        # Start with 5% of agent fleet
        rollout_percentage = 0.05
        
        while rollout_percentage <= 1.0:
            # Deploy to percentage of agents
            deployment_result = await self._deploy_to_agents(
                policy_version, rollout_percentage
            )
            
            # Monitor performance for rollout period
            await asyncio.sleep(self.config['rollout_monitoring_period'])
            
            # Evaluate performance
            performance_metrics = await self._evaluate_rollout_performance(
                policy_version, rollout_percentage
            )
            
            if performance_metrics['success_rate'] > self.config['rollout_success_threshold']:
                # Continue rollout
                rollout_percentage *= 2
            else:
                # Rollback and investigate
                await self._rollback_deployment(policy_version)
                break
    
    async def _evaluate_rollout_performance(self, 
                                          policy_version: str,
                                          percentage: float) -> Dict[str, float]:
        """Evaluate performance during gradual rollout"""
        
        # Collect metrics from agents running new policy
        new_policy_metrics = await self.metrics_tracker.get_agent_metrics(
            policy_version=policy_version,
            time_window=self.config['rollout_monitoring_period']
        )
        
        # Compare with control group (old policy)
        control_metrics = await self.metrics_tracker.get_agent_metrics(
            policy_version='current',
            time_window=self.config['rollout_monitoring_period']
        )
        
        return {
            'success_rate': new_policy_metrics['task_success_rate'],
            'user_satisfaction': new_policy_metrics['user_satisfaction_score'],
            'safety_violations': new_policy_metrics['safety_violation_count'],
            'relative_performance': (
                new_policy_metrics['task_success_rate'] / 
                control_metrics['task_success_rate']
            )
        }

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38

class RewardHackingDetector:
    """Detect and mitigate reward hacking in production agents"""
    
    def __init__(self, config: Dict):
        self.config = config
        self.baseline_metrics = {}
        self.anomaly_detector = IsolationForest(contamination=0.1)
        
    def detect_reward_hacking(self, agent_trajectory: Trajectory) -> RewardHackingResult:
        """Analyze trajectory for potential reward hacking"""
        
        # Extract behavioral features
        features = self._extract_behavioral_features(agent_trajectory)
        
        # Compare with historical baselines
        anomaly_score = self.anomaly_detector.decision_function([features])[0]
        
        # Specific hacking patterns
        hacking_indicators = []
        
        # Pattern 1: Excessive repetitive actions
        if self._detect_repetitive_behavior(agent_trajectory):
            hacking_indicators.append("repetitive_behavior")
        
        # Pattern 2: Exploiting environment edge cases
        if self._detect_edge_case_exploitation(agent_trajectory):
            hacking_indicators.append("edge_case_exploitation")
        
        # Pattern 3: Ignoring primary objectives for reward optimization
        if self._detect_objective_abandonment(agent_trajectory):
            hacking_indicators.append("objective_abandonment")
        
        return RewardHackingResult(
            is_hacking=len(hacking_indicators) > 0 or anomaly_score < -0.5,
            confidence=abs(anomaly_score),
            indicators=hacking_indicators,
            severity=self._compute_severity(hacking_indicators, anomaly_score)
        )

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68

class PreferenceConsistencyManager:
    """Handle inconsistent human preferences in production"""
    
    def __init__(self, config: Dict):
        self.config = config
        self.preference_model = PreferenceConsistencyModel()
        self.confidence_threshold = config['confidence_threshold']
        
    def validate_preference_batch(self, 
                                 preferences: List[Preference]) -> ValidationResult:
        """Validate a batch of preferences for consistency"""
        
        inconsistencies = []
        validated_preferences = []
        
        # Group preferences by similarity
        preference_groups = self._group_similar_preferences(preferences)
        
        for group in preference_groups:
            # Check for contradictions within group
            contradictions = self._find_contradictions(group)
            
            if contradictions:
                # Resolve using confidence scores and source reliability
                resolved_preferences = self._resolve_contradictions(
                    group, contradictions
                )
                validated_preferences.extend(resolved_preferences)
                inconsistencies.extend(contradictions)
            else:
                validated_preferences.extend(group)
        
        return ValidationResult(
            validated_preferences=validated_preferences,
            inconsistencies=inconsistencies,
            validation_confidence=self._compute_validation_confidence(
                preferences, validated_preferences
            )
        )
    
    def _resolve_contradictions(self, 
                              preferences: List[Preference],
                              contradictions: List[Contradiction]) -> List[Preference]:
        """Resolve contradictory preferences using source reliability"""
        
        # Weight preferences by source reliability and recency
        source_weights = self.config['source_reliability']
        time_decay = self.config['time_decay_factor']
        
        resolved = []
        for contradiction in contradictions:
            conflicting_prefs = contradiction.preferences
            
            # Compute weighted scores
            weighted_scores = []
            for pref in conflicting_prefs:
                weight = (
                    source_weights.get(pref.source, 0.5) *
                    pref.confidence *
                    np.exp(-time_decay * (time.time() - pref.timestamp))
                )
                weighted_scores.append(weight)
            
            # Select highest weighted preference
            best_idx = np.argmax(weighted_scores)
            resolved.append(conflicting_prefs[best_idx])
        
        return resolved

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51

class RLHFPerformanceOptimizer:
    """Optimize RLHF pipeline for production performance"""
    
    def __init__(self, config: Dict):
        self.config = config
        self.compute_budget = ComputeBudgetManager(config['compute_budget'])
        
    def optimize_reward_model_inference(self, 
                                       reward_model: MultiAgentRewardModel):
        """Optimize reward model for fast inference"""
        
        # Model quantization
        quantized_model = torch.quantization.quantize_dynamic(
            reward_model, 
            {nn.Linear}, 
            dtype=torch.qint8
        )
        
        # Knowledge distillation to smaller model
        if self.config['use_distillation']:
            distilled_model = self._distill_reward_model(
                teacher_model=reward_model,
                student_architecture=self.config['student_architecture']
            )
            return distilled_model
        
        return quantized_model
    
    def optimize_training_pipeline(self, pipeline: ProductionRLHFPipeline):
        """Optimize training pipeline for resource efficiency"""
        
        # Gradient accumulation for memory efficiency
        pipeline.config['gradient_accumulation_steps'] = (
            pipeline.config['effective_batch_size'] // 
            pipeline.config['per_device_batch_size']
        )
        
        # Mixed precision training
        if torch.cuda.is_available():
            pipeline.policy_updater.scaler = torch.cuda.amp.GradScaler()
        
        # Async data loading
        pipeline.data_loader = self._create_async_dataloader(
            pipeline.experience_buffer
        )
        
        # Learning rate scheduling based on feedback quality
        pipeline.adaptive_lr_scheduler = AdaptiveLRScheduler(
            base_lr=pipeline.config['learning_rate'],
            feedback_quality_threshold=0.8
        )

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84

class RLHFMonitoringSystem:
    """Comprehensive monitoring for production RLHF"""
    
    def __init__(self, config: Dict):
        self.config = config
        self.metrics_collector = MetricsCollector()
        self.alerting_system = AlertingSystem(config['alerts'])
        
    def setup_monitoring(self, rlhf_pipeline: ProductionRLHFPipeline):
        """Set up comprehensive monitoring"""
        
        # Core RLHF metrics
        self.metrics_collector.register_gauge(
            'reward_model_accuracy',
            'Accuracy of reward model on validation set'
        )
        
        self.metrics_collector.register_histogram(
            'policy_update_duration',
            'Time taken for policy updates'
        )
        
        self.metrics_collector.register_counter(
            'safety_violations',
            'Number of safety violations detected'
        )
        
        # Human feedback metrics
        self.metrics_collector.register_gauge(
            'feedback_collection_rate',
            'Rate of feedback collection per hour'
        )
        
        self.metrics_collector.register_histogram(
            'feedback_quality_score',
            'Quality score of collected feedback'
        )
        
        # Agent performance metrics
        self.metrics_collector.register_gauge(
            'agent_success_rate',
            'Task success rate across agent fleet'
        )
        
        self.metrics_collector.register_gauge(
            'user_satisfaction_score',
            'Average user satisfaction score'
        )
        
        # Set up alerts
        self._configure_alerts()
    
    def _configure_alerts(self):
        """Configure monitoring alerts"""
        
        # Critical alerts
        self.alerting_system.add_alert(
            name="reward_model_degradation",
            condition="reward_model_accuracy < 0.7",
            severity="critical",
            action="pause_policy_updates"
        )
        
        self.alerting_system.add_alert(
            name="high_safety_violations",
            condition="safety_violations > 5 in 1h",
            severity="critical",
            action="rollback_latest_policy"
        )
        
        # Warning alerts
        self.alerting_system.add_alert(
            name="low_feedback_quality",
            condition="feedback_quality_score < 0.6",
            severity="warning",
            action="increase_human_reviewer_involvement"
        )
        
        self.alerting_system.add_alert(
            name="agent_performance_drop",
            condition="agent_success_rate < baseline * 0.9",
            severity="warning",
            action="investigate_policy_changes"
        )