> ## Documentation Index > Fetch the complete documentation index at: https://docs.monostate.ai/llms.txt > Use this file to discover all available pages before exploring further. # Roadmap > Upcoming features and training tasks # Roadmap We're continuously expanding AITraining's capabilities. Here's what's currently supported and what's coming next. ## Currently Supported ### LLM Training * **SFT** - Supervised Fine-Tuning for instruction following * **DPO** - Direct Preference Optimization * **ORPO** - Odds Ratio Preference Optimization * **PPO** - Proximal Policy Optimization (RL) * **Reward Modeling** - Train reward models for RLHF * **Knowledge Distillation** - Transfer knowledge from larger models ### Text Tasks * **Text Classification** - Sentiment, spam detection, categorization * **Token Classification** - NER, POS tagging, entity extraction * **Sequence-to-Sequence** - Translation, summarization * **Extractive QA** - Answer questions from context * **Sentence Transformers** - Semantic similarity embeddings ### Vision Tasks * **Image Classification** - Categorize images into labels * **Image Regression** - Predict continuous values from images * **Object Detection** - Locate and identify objects in images * **Vision-Language Models** - Multimodal image+text tasks ### Tabular Data * **XGBoost** - Gradient boosting * **LightGBM** - Fast gradient boosting * **Random Forest** - Ensemble decision trees * **CatBoost** - Categorical feature handling * **ExtraTrees** - Extremely randomized trees ### Reinforcement Learning * **PPO Trainer** - Proximal Policy Optimization for LLMs * **DPO Trainer** - Direct Preference Optimization * **Reward Models** - Standard, pairwise, and multi-objective * **RL Environments** - Text generation, math problems, code generation * **Async Forward-Backward Pipeline** - Efficient training pipeline *** ## Planned Training Tasks ### Vision Tasks (Planned) | Task | Description | Status | | --------------------- | -------------------------------------------------------------------------------- | ------- | | Image Segmentation | Pixel-level labeling for medical imaging, satellite analysis, background removal | Planned | | Semantic Segmentation | Scene understanding with class labels per pixel | Planned | | Instance Segmentation | Detect and segment individual object instances | Planned | | Panoptic Segmentation | Combined semantic + instance segmentation | Planned | ### Time Series & Forecasting (Planned) | Task | Description | Status | | -------------------------- | ----------------------------------------------------------- | ------- | | Time Series Forecasting | Predict future values (stock prices, demand, weather) | Planned | | Anomaly Detection | Identify outliers in sequential data | Planned | | Time Series Classification | Classify sequences (ECG, sensor data, activity recognition) | Planned | ### Additional ML Algorithms (Planned) | Task | Description | Status | | ------------------------- | ------------------------------------------ | ------- | | Support Vector Machines | SVMs for classification and regression | Planned | | K-Nearest Neighbors | Instance-based learning | Planned | | Gaussian Processes | Probabilistic predictions with uncertainty | Planned | | Neural Networks (sklearn) | Simple MLPs for tabular data | Planned | ### Specialized LLM Training (Planned) | Task | Description | Status | | --------------------------- | ----------------------------------------------- | ------- | | Code LLM Fine-tuning | Specialized training for code generation models | Planned | | Math Reasoning | Train models for mathematical problem solving | Planned | | Multi-turn Dialogue | Enhanced conversation modeling | Planned | | Tool Use / Function Calling | Train models to use external tools | Planned | | Agentic Behaviors | Train models for autonomous task completion | Planned | ### Audio & Speech (Planned) | Task | Description | Status | | ------------------------ | ------------------------------------------------- | ------- | | Speech Recognition (ASR) | Automatic speech-to-text | Planned | | Text-to-Speech (TTS) | Voice synthesis and cloning | Planned | | Audio Classification | Sound event detection, music genre classification | Planned | | Speaker Diarization | Identify who spoke when | Planned | ### Multimodal (Planned) | Task | Description | Status | | ------------------------- | ---------------------------------------- | ------- | | Video Understanding | Action recognition, video captioning | Planned | | Document AI | Layout analysis, form understanding | Planned | | Chart/Graph Understanding | Extract data from visualizations | Planned | | 3D Vision | Point cloud processing, depth estimation | Planned | ### Specialized Domains (Planned) | Task | Description | Status | | ----------------------- | --------------------------------------------- | ------- | | Medical/Clinical NLP | HIPAA-aware training for healthcare | Planned | | Legal Document Analysis | Contract review, case law search | Planned | | Scientific Literature | Paper parsing, citation analysis | Planned | | Financial Analysis | Sentiment, risk assessment, report generation | Planned | ## Planned Features ### Training Enhancements * [ ] Ray Tune integration for distributed sweeps * [ ] Curriculum learning support * [ ] Continual learning / catastrophic forgetting prevention * [ ] Mixture of Experts (MoE) fine-tuning * [ ] Speculative decoding training ### Infrastructure * [ ] Full TUI (Terminal User Interface) wizard * [ ] Web-based training UI * [ ] Kubernetes deployment templates * [ ] AWS/GCP/Azure marketplace images ### Evaluation * [ ] Automated red-teaming * [ ] Bias and fairness benchmarks * [ ] Domain-specific evaluation suites * [ ] Human preference collection interface ## Vote for Features Want to influence our priorities? Let us know what matters most to you: Vote on feature requests and propose new ideas Join the discussion and share your use cases ## Contributing Interested in helping build these features? We welcome contributions: * **Core Development**: Python, PyTorch, Transformers * **Documentation**: Help us document new features * **Testing**: Test new trainers and report issues * **Examples**: Share your training recipes See our [GitHub repository](https://github.com/monostate/aitraining) for contribution guidelines. ## Release Notes For current features and recent updates, see the [Changelog](/changelog).