HRM Autonomous Agent System

An advanced autonomous agent system that continuously trains and improves a Hierarchical Reasoning Model (HRM) with enhanced capabilities for reasoning, instruction following, tool use, and error correction.

Features

🧠 Enhanced Hierarchical Reasoning Model

• Adaptive Computation Time (ACT): Dynamic reasoning steps with halt mechanism
• Tool Use Integration: Built-in function calling and tool selection capabilities
• Scratchpad Memory: Persistent memory across reasoning steps
• Error Detection & Correction: Self-monitoring and improvement mechanisms
• Confidence Calibration: Uncertainty estimation for better decision making

🔄 Autonomous Training System

• Continuous Learning: On-the-fly data collection and model updates
• Self-Improvement: Automatic strategy adjustment based on performance
• Multi-objective Optimization: Balances reasoning, efficiency, and accuracy
• Adaptive Hyperparameters: Dynamic learning rate and architecture adjustments
• Human-in-the-loop: Intervention system for critical errors

📊 Comprehensive Data Collection

• Multi-source Aggregation: Reasoning, instruction, tool use, and conversation data
• Quality Filtering: Automatic data cleaning and validation
• Diversity Sampling: Ensures balanced training distribution
• Real-time Collection: API integration for fresh data sources
• Synthetic Generation: Creates targeted training examples

🎯 Advanced Evaluation System

• Multi-dimensional Assessment: Reasoning, instruction following, tool use, error correction
• Benchmark Integration: GSM8K, MATH, Alpaca, and custom evaluations
• Confidence Calibration: Measures uncertainty alignment
• Efficiency Metrics: Reasoning steps vs. performance trade-offs
• Continuous Monitoring: Real-time performance tracking

🌐 Interactive Web Interface

• Real-time Dashboard: Live training progress and metrics
• Configuration Management: Dynamic parameter adjustment
• Visualization: Training curves, performance radar charts, data distribution
• Control Panel: Start/stop training, trigger evaluations, collect data
• Alert System: Notifications for critical events and interventions

Installation

Prerequisites

• Python 3.8+
• CUDA-capable GPU (recommended)
• 16GB+ RAM
• 50GB+ storage space

Setup

# Clone the repository
git clone <repository-url>
cd Auto-HRM

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Create necessary directories
mkdir -p data/{raw,processed,reasoning,instruction_following,tool_use,error_correction}
mkdir -p checkpoints outputs logs

Configuration

# Generate default configuration
python main.py --save-config config/default.json

# Edit configuration as needed
nano config/default.json

Usage

Web Interface (Recommended)

# Start the web dashboard
python main.py --mode web --host 0.0.0.0 --port 5000

# Access dashboard at http://localhost:5000

Command Line Interface

Autonomous Training

# Start autonomous training with default settings
python main.py --mode autonomous

# With custom configuration
python main.py --mode autonomous --config config/my_config.json

# With specific parameters
python main.py --mode autonomous \
    --batch-size 32 \
    --learning-rate 1e-5 \
    --epochs 5 \
    --max-time 48

Data Collection Only

# Collect training data
python main.py --mode collect

Model Evaluation Only

# Evaluate existing model
python main.py --mode evaluate --checkpoint checkpoints/best_model.pt

Architecture

Core Components

1. Enhanced HRM Model (core/hrm_model.py)

• HRMBlock: Transformer block with RMSNorm and SwiGLU
• ToolUseHead: Function calling decision mechanism
• ScratchpadModule: Working memory for multi-step reasoning
• Self-Evaluation: Performance monitoring and improvement suggestions

2. Autonomous Trainer (training/autonomous_trainer.py)

• ContinuousDataset: Dynamic dataset that grows during training
• Performance Monitoring: Real-time metrics and plateau detection
• Self-Improvement Cycle: Automatic strategy adjustments
• Checkpoint Management: Automatic saving and recovery

3. Data Collector (data_collection/data_collector.py)

• Multi-source Integration: HuggingFace datasets, APIs, web scraping
• Quality Assurance: Content filtering and validation
• Diversity Sampling: Balanced data distribution
• Synthetic Generation: Targeted example creation

4. Evaluator (evaluation/evaluator.py)

• Comprehensive Metrics: 7 evaluation dimensions
• Benchmark Integration: Standard and custom test sets
• Confidence Assessment: Uncertainty calibration
• Trend Analysis: Performance trajectory monitoring

Training Process

1. Initialization: Load model, setup optimizer, initialize components
2. Data Collection: Gather diverse training examples from multiple sources
3. Training Epoch: Process batches with gradient accumulation and mixed precision
4. Evaluation: Assess performance across multiple dimensions
5. Self-Improvement: Analyze results and adjust strategies
6. Checkpoint: Save model state and training progress
7. Monitoring: Check for intervention needs or completion criteria

Data Flow

Data Sources → Data Collector → Quality Filter → Continuous Dataset
                                                        ↓
Web Interface ← Status Updates ← Autonomous Trainer ← Model Training
                                        ↓
                                   Evaluator → Performance Metrics

Configuration

Training Parameters

{
  "batch_size": 16,
  "learning_rate": 2e-5,
  "num_epochs": 3,
  "max_training_time": 24,
  "performance_threshold": 0.85,
  "mixed_precision": true,
  "gradient_accumulation_steps": 1
}

Model Parameters

{
  "d_model": 512,
  "n_heads": 8,
  "d_ff": 2048,
  "dropout": 0.1,
  "halt_max_steps": 8,
  "ponder_loss_weight": 1e-2,
  "num_tools": 100
}

Data Collection

{
  "max_daily_samples": 10000,
  "quality_threshold": 0.7,
  "data_mixing_ratios": {
    "reasoning": 0.4,
    "instruction": 0.3,
    "tool_use": 0.2,
    "error_correction": 0.1
  }
}

Monitoring and Control

Web Dashboard Features

• Real-time Status: Training progress, current metrics, system health
• Interactive Charts: Loss curves, performance radar, data distribution
• Control Panel: Start/stop training, trigger evaluations, collect data
• Configuration: Dynamic parameter adjustment
• Logs: Real-time system messages and error tracking
• Alerts: Notifications for critical events

Performance Metrics

• Reasoning Accuracy: Mathematical and logical problem solving
• Instruction Following: Task completion and format adherence
• Tool Use Success: Function calling and tool selection accuracy
• Error Correction: Detection and fixing of mistakes
• Overall Confidence: Uncertainty calibration quality
• Reasoning Efficiency: Steps vs. performance trade-off
• Response Quality: Coherence, relevance, and completeness

Advanced Features

Self-Improvement Mechanisms

• Performance Analysis: Identifies weaknesses and improvement opportunities
• Strategy Adjustment: Modifies training approach based on results
• Hyperparameter Adaptation: Dynamic learning rate and architecture changes
• Data Augmentation: Generates targeted training examples
• Error Pattern Recognition: Learns from common mistakes

Human-in-the-Loop

• Intervention Triggers: Automatic detection of critical issues
• Manual Override: Web interface controls for human guidance
• Feedback Integration: Incorporates human corrections and preferences
• Safety Mechanisms: Prevents harmful or incorrect outputs

Deployment Options

• Local Training: Single machine with GPU acceleration
• Cloud Integration: Supports cloud-based training infrastructure
• Distributed Training: Multi-GPU and multi-node capabilities
• Edge Deployment: Optimized models for resource-constrained environments

Troubleshooting

Common Issues

CUDA Out of Memory

# Reduce batch size
python main.py --mode autonomous --batch-size 8

# Enable gradient checkpointing (add to config)
"gradient_checkpointing": true

Data Collection Failures

# Check internet connection and API keys
# Review logs for specific error messages
tail -f hrm_agent.log

Training Stagnation

• Check learning rate (may be too high/low)
• Verify data quality and diversity
• Review performance plateau settings
• Consider architecture adjustments

Web Interface Issues

# Check port availability
netstat -tulpn | grep :5000

# Restart with different port
python main.py --mode web --port 8080

Performance Optimization

Memory Usage

• Use mixed precision training
• Implement gradient checkpointing
• Optimize batch size for your hardware
• Clear cache regularly

Training Speed

• Use multiple GPUs if available
• Optimize data loading with multiple workers
• Use compiled models (torch.compile)
• Profile bottlenecks with torch.profiler

Contributing

Development Setup

# Install development dependencies
pip install -r requirements.txt

# Install pre-commit hooks
pre-commit install

# Run tests
pytest tests/

# Format code
black .
flake8 .

Adding New Features

1. Data Sources: Extend DataCollector with new collection methods
2. Evaluation Metrics: Add new assessment dimensions to ModelEvaluator
3. Model Components: Enhance EnhancedHierarchicalReasoningModel
4. Training Strategies: Modify AutonomousTrainer improvement cycles
5. Web Interface: Add new dashboard components and visualizations

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use this system in your research, please cite:

@software{hrm_autonomous_agent,
  title={HRM Autonomous Agent: Continuous Learning System for Hierarchical Reasoning},
  author={Your Name},
  year={2024},
  url={https://github.com/your-repo/hrm-autonomous-agent}
}

Acknowledgments

• Based on the Hierarchical Reasoning Model architecture
• Inspired by Adaptive Computation Time mechanisms
• Built with PyTorch, Transformers, and modern ML tools
• Web interface powered by Flask and Plotly

Support

For questions, issues, or contributions:

• Open an issue on GitHub
• Check the documentation
• Review the troubleshooting guide
• Contact the development team

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Note: This system is designed for research and educational purposes. Ensure proper safety measures and human oversight when deploying in production environments.