trainStep

Executes a single incremental learning step using new interaction data.

This method implements the core adaptive learning capability that enables KARL to continuously improve its understanding of user behavior. Each training step processes one interaction event and updates the underlying model to incorporate new patterns while preserving previously learned knowledge.

Incremental learning approach:

• Online learning: Updates model immediately upon receiving new data

• Streaming processing: Handles continuous data flows without requiring batch accumulation

• Memory efficiency: Avoids storing large datasets by learning from individual samples

• Real-time adaptation: Enables immediate response to changing user behavior patterns

Learning process:

1. Feature extraction: Convert interaction data to numerical features for model input

2. Context integration: Combine new data with recent interaction history for context

3. Model update: Apply learning algorithms to adjust weights and parameters

4. Pattern validation: Verify that new patterns don't contradict established knowledge

5. Performance monitoring: Track learning metrics and adaptation effectiveness

Data processing pipeline:

Input validation:

• Verify interaction data format and completeness

• Check for data quality issues (missing fields, invalid timestamps)

• Filter out noise, duplicates, or irrelevant interactions

• Apply privacy filters to exclude sensitive information

Feature engineering:

• Extract temporal patterns (time of day, frequency, duration)

• Encode categorical data (action types, context categories)

• Normalize numerical features for stable learning

• Generate derived features from interaction sequences

Learning algorithm execution:

• Apply appropriate learning algorithm (gradient descent, reinforcement learning, etc.)

• Update model weights using calculated gradients and learning rates

• Adjust hyperparameters based on learning progress and performance

• Implement regularization to prevent overfitting to recent data

Model validation and quality control:

• Check for catastrophic forgetting of previous patterns

• Validate that model outputs remain within expected ranges

• Monitor prediction accuracy on recent validation samples

• Detect and handle concept drift in user behavior

Asynchronous execution patterns:

• Training may be computationally expensive and should not block calling thread

• Background processing allows UI responsiveness during intensive learning

• Job-based approach enables cancellation and progress monitoring

• Error handling and recovery can be managed independently

Performance optimization strategies:

• Micro-batching: Accumulate multiple interactions for more efficient processing

• Adaptive scheduling: Vary learning frequency based on data volume and patterns

• Resource throttling: Limit CPU/memory usage to maintain system responsiveness

• Smart caching: Cache intermediate results to avoid redundant computations

Error handling and robustness:

• Invalid data should be logged and ignored rather than causing failures

• Model instability should trigger rollback to previous stable state

• Resource exhaustion should gracefully degrade rather than crash

• Network or IO failures during training should be retried with backoff

Integration with broader KARL system:

• Coordinate with prediction requests to avoid conflicts during model updates

• Trigger state persistence after significant learning milestones

• Respect user instructions that may modify learning behavior

• Provide progress updates for UI components and monitoring systems

Parameters

See also

Throws