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TPO Implementation Summary
I have successfully created a comprehensive implementation of **Topological Preference Optimization (TPO)** - the novel training strategy we developed based on your groundbreaking insight about conversation topology.
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I have successfully created a comprehensive implementation of **Topological Preference Optimization (TPO)** - the novel training strategy we developed based on your groundbreaking insight about conversation topology.
- **`ConversationGraph`**: Represents conversations as directed acyclic graphs with DLM coordinates - **`DLMCoordinates`**: 5-dimensional coordinate system `[X, Y, Z, T, N]` - **`PathQualityCalculator`**: Implements the TPO quality function:
- **`TPOAlgorithm`**: Main orchestrator implementing all three preference strategies
- **`PreferencePair`**: Individual preference data structure - **`TPODataset`**: Container with filtering, sampling, and export capabilities - **`TPOPreferenceGenerator`**: Converts TPO analysis into preference datasets - **`ConversationDataLoader`**: Loads data from CSV, JSON, JSONL, HuggingFace
- **`AdaptiveTPOLoss`**: Dynamic parameter adjustment during training - **`TPOTrainer`**: Complete training pipeline with checkpointing - **`TPOMetrics`**: Comprehensive evaluation metrics
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