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RuvLLM Training Module

Fine-tuning dataset generation for RuvLTRA models, focusing on Claude Flow agent task routing and model selection.

SOTA Achievements (v2.3)

Metric Before After Method
Hybrid Routing Accuracy 95% 100% Keyword-First + Embedding Fallback
Embedding-Only Accuracy 45% 88.2% Contrastive Learning (Triplet + InfoNCE)
Hard Negative Accuracy N/A 81.2% Claude-Generated Confusing Pairs
Agent Types Supported 13 13 All Claude Code agent types

Training Data (v2.3 SOTA)

  • Base triplets: 578 examples from Claude Code routing data
  • Claude-generated hard negatives: 500+ high-quality confusing pairs
  • Total training set: 1,078 triplets
  • Hard negative ratio: 48.4% (up from 18%)

Training Pipeline

┌──────────────────┐     ┌──────────────────┐     ┌──────────────────┐
│  Hard Negative   │────►│  Contrastive     │────►│  GRPO Feedback   │
│  Generation      │     │  Training        │     │  Loop            │
│  (Claude Opus)   │     │  (Candle/Metal)  │     │  (Claude Judge)  │
└──────────────────┘     └──────────────────┘     └──────────────────┘
                                  │
                                  ▼
                         ┌──────────────────┐
                         │  GGUF Export     │
                         │  (Adapter Merge) │
                         └──────────────────┘

Overview

The training module generates synthetic datasets for fine-tuning RuvLTRA models on two key tasks:

  1. Agent Routing: Classify tasks to appropriate Claude Flow agents (Coder, Researcher, Security, Architecture, Reviewer)
  2. Model Selection: Route tasks to optimal Claude models (Haiku/Sonnet/Opus) based on complexity

Real Contrastive Training (v2.3 - Production)

The real_trainer module provides production-grade training with actual Candle weight updates:

use ruvllm::training::{RealContrastiveTrainer, RealTrainingConfig, run_training_pipeline};
use std::path::PathBuf;

// Option 1: Full pipeline with GRPO feedback
#[tokio::main]
async fn main() -> Result<(), String> {
    run_training_pipeline(
        &PathBuf::from("~/.ruvllm/training/combined-sota.jsonl"),
        &PathBuf::from("ruvltra-claude-code-0.5b-q4_k_m.gguf"),
        &PathBuf::from("ruvltra-claude-code-sota.gguf"),
        Some(&std::env::var("ANTHROPIC_API_KEY").unwrap()), // For GRPO
    ).await
}

// Option 2: Manual training with fine-grained control
let config = RealTrainingConfig {
    model_path: PathBuf::from("ruvltra-claude-code-0.5b-q4_k_m.gguf"),
    output_path: PathBuf::from("ruvltra-claude-code-sota.gguf"),
    learning_rate: 2e-5,
    weight_decay: 0.01,
    batch_size: 16,
    epochs: 30,
    margin: 0.5,           // Triplet loss margin
    temperature: 0.07,     // InfoNCE temperature
    embedding_dim: 896,    // Qwen 0.5B embedding size
    use_metal: true,       // Apple Silicon GPU acceleration
    enable_grpo: true,     // Enable GRPO reward scaling
    ..Default::default()
};

let mut trainer = RealContrastiveTrainer::new(config)?;
trainer.load_triplets("combined-sota.jsonl")?;

// Train with real weight updates
let result = trainer.train()?;
println!("Best accuracy: {:.2}%", result.best_accuracy * 100.0);

// Export to GGUF format
let export = trainer.export_gguf("output.gguf")?;
println!("Exported {} weights to {}", export.total_weights, export.weights_path.display());

GGUF Export

The trainer exports adapter weights that can be merged with the base Qwen model:

# After training, merge adapter with base model
bash output.gguf.weights/merge_adapter.sh

# Files created:
# - output.gguf.weights/adapter_weights.bin  (binary weights)
# - output.gguf.weights/metadata.json        (training config)
# - output.gguf.weights/merge_adapter.sh     (merge script)

GRPO Feedback Loop

GRPO (Group Relative Policy Optimization) uses Claude as a judge to improve training:

use ruvllm::training::{GrpoEvaluator, GrpoFeedback};

let evaluator = GrpoEvaluator::new(api_key);

// Evaluate predictions
let predictions = vec![
    ("Add error handling".to_string(), "coder".to_string(), "coder".to_string()),
    ("Review the PR".to_string(), "reviewer".to_string(), "tester".to_string()),
];

let feedback = evaluator.evaluate(&predictions).await?;
for fb in feedback {
    trainer.add_grpo_feedback(fb);
}

// Re-train with GRPO-enhanced loss scaling
let result = trainer.train()?;

Contrastive Learning (Simulated)

The contrastive module provides state-of-the-art embedding fine-tuning:

use ruvllm::training::{ContrastiveTrainer, ContrastiveConfig, TrainingTriplet};

// Configure contrastive training
let config = ContrastiveConfig {
    learning_rate: 2e-5,
    margin: 0.5,           // Triplet loss margin
    temperature: 0.07,     // InfoNCE temperature
    batch_size: 32,
    embedding_dim: 896,    // Qwen 0.5B embedding size
    hard_negative_ratio: 0.18,
    use_metal: true,       // Apple Silicon GPU
    ..Default::default()
};

// Initialize and train
let mut trainer = ContrastiveTrainer::new(config)?;
trainer.load_triplets("triplets.jsonl")?;
let result = trainer.train(30)?;  // 30 epochs

println!("Final accuracy: {:.2}%", result.final_accuracy * 100.0);

Claude-Powered Hard Negative Generation

Generate high-quality confusing training pairs using Claude Opus 4.5:

node scripts/training/claude-hard-negatives.js --count=10 --grpo

# Output: ~/.ruvllm/training/claude-hard-negatives.jsonl

This generates triplets for confusing agent pairs:

  • coder vs refactorer (both modify code)
  • researcher vs architect (both analyze)
  • reviewer vs tester (both validate)
  • debugger vs optimizer (both fix issues)
  • And 6 more confusing pairs...

Quick Start

use ruvllm::training::{DatasetGenerator, DatasetConfig};

// Generate dataset with 100 examples per category
let config = DatasetConfig::default();
let mut generator = DatasetGenerator::new(config);
let dataset = generator.generate();

// Export to JSONL
dataset.export_jsonl("training.jsonl")?;

// Split for training/validation/test
let (train, val, test) = dataset.split(0.7, 0.15, 0.15, 42);

Task Categories

1. Coder (20% of dataset)

  • Focus: Code generation, debugging, refactoring
  • Examples:
    • "Implement JWT authentication middleware in TypeScript"
    • "Debug memory leak in request handler"
    • "Refactor UserService to use dependency injection"

Model Routing:

  • Simple tasks → Haiku (quick fixes, simple functions)
  • Moderate tasks → Sonnet (components, APIs)
  • Complex tasks → Opus (algorithms, system-level)

2. Researcher (20% of dataset)

  • Focus: Analysis, exploration, documentation
  • Examples:
    • "Analyze GraphQL performance bottlenecks"
    • "Research best practices for microservices"
    • "Document REST API endpoints"

Model Routing:

  • Simple tasks → Haiku (basic docs)
  • Moderate/Complex → Sonnet (analysis, research)

3. Security (20% of dataset)

  • Focus: Audit, vulnerability analysis, threat detection
  • Examples:
    • "Audit authentication flow for security vulnerabilities"
    • "Review cryptographic key management"
    • "Identify SQL injection attack vectors"

Model Routing:

  • All tasks → Opus (security requires highest quality)

4. Architecture (20% of dataset)

  • Focus: System design, planning, architecture
  • Examples:
    • "Design microservices architecture for e-commerce"
    • "Plan database schema for multi-tenant SaaS"
    • "Architect real-time event streaming pipeline"

Model Routing:

  • Simple tasks → Sonnet (basic schemas)
  • Moderate/Complex → Opus (distributed systems)

5. Reviewer (20% of dataset)

  • Focus: Code review, quality assessment
  • Examples:
    • "Review pull request #123 for best practices"
    • "Assess code quality of UserController"
    • "Review error handling in payment service"

Model Routing:

  • Simple tasks → Haiku (standards compliance)
  • Moderate/Complex → Sonnet (quality, architecture review)

Dataset Configuration

use ruvllm::training::{DatasetConfig, AugmentationConfig};

let config = DatasetConfig {
    // Base examples per category
    examples_per_category: 100,

    // Enable data augmentation
    enable_augmentation: true,

    // Augmentation settings
    augmentation: AugmentationConfig {
        // Generate 2 paraphrases per example
        paraphrases_per_example: 2,

        // Generate 2 complexity variations
        complexity_variations: 2,

        // Enable domain transfer
        enable_domain_transfer: true,
    },

    // Random seed for reproducibility
    seed: 42,
};

Dataset Size Calculation

With default configuration:

  • Base examples: 5 categories × 100 = 500 examples
  • Paraphrases: 500 × 2 = 1,000 additional examples
  • Complexity variations: 500 × 2 = ~800 additional examples (some filtered)
  • Domain transfer: 500 × 1 = ~400 additional examples (some filtered)
  • Total: ~2,700 examples (actual varies due to filtering)

Data Augmentation

1. Paraphrasing

Replaces words with synonyms to increase linguistic diversity:

Original:    "Implement a function to validate user input"
Paraphrased: "Create a function to validate user input"
             "Build a function to validate user input"

2. Complexity Variations

Creates examples at different complexity levels:

Simple:   "Add error handling to API endpoint"
Moderate: "Implement error handling with retry logic"
Complex:  "Design fault-tolerant error handling with circuit breakers"

3. Domain Transfer

Applies task patterns across technical domains:

Web:      "Optimize React component rendering"
Mobile:   "Optimize Flutter widget rendering"
Systems:  "Optimize kernel thread scheduling"

Export Formats

JSONL (Streaming Format)

// One JSON object per line
dataset.export_jsonl("training.jsonl")?;

Example line:

{"input":"Implement authentication middleware","context":"JWT with RS256","output_agent":"coder","metadata":{"category":"Coder","complexity":"Moderate","domain":"Web","expected_model":"sonnet","quality_score":0.87,"tags":["auth","middleware"]}}

JSON (Full Array)

// Human-readable JSON array
dataset.export_json("training.json")?;

Statistics

// Export dataset statistics
dataset.export_stats("stats.json")?;

Stats format:

{
  "total_examples": 2700,
  "examples_per_category": {
    "coder": 540,
    "researcher": 540,
    "security": 540,
    "architecture": 540,
    "reviewer": 540
  },
  "examples_per_complexity": {
    "Simple": 900,
    "Moderate": 1080,
    "Complex": 720
  },
  "avg_quality_score": 0.87
}

Dataset Splits

// 70% train, 15% validation, 15% test
let (train, val, test) = dataset.split(0.7, 0.15, 0.15, 42);

// Export each split
ClaudeTaskDataset::new(train).export_jsonl("train.jsonl")?;
ClaudeTaskDataset::new(val).export_jsonl("val.jsonl")?;
ClaudeTaskDataset::new(test).export_jsonl("test.jsonl")?;

Example Structure

ClaudeTaskExample

pub struct ClaudeTaskExample {
    /// Task description (model input)
    pub input: String,

    /// Additional context
    pub context: String,

    /// Expected agent (target output)
    pub output_agent: String,

    /// Task metadata
    pub metadata: TaskMetadata,
}

TaskMetadata

pub struct TaskMetadata {
    /// Task category
    pub category: TaskCategory,

    /// Complexity level (Simple/Moderate/Complex)
    pub complexity: ComplexityLevel,

    /// Technical domain
    pub domain: DomainType,

    /// Recommended Claude model
    pub expected_model: String,

    /// Quality score (0.0-1.0)
    pub quality_score: f32,

    /// Descriptive tags
    pub tags: Vec<String>,
}

Model Selection Logic

The dataset includes intelligent model routing based on task category and complexity:

Category Simple Moderate Complex
Coder Haiku Sonnet Opus
Researcher Haiku Sonnet Sonnet
Security Opus Opus Opus
Architecture Sonnet Opus Opus
Reviewer Haiku Sonnet Sonnet

Cost Optimization:

  • Haiku: ~75% cheaper than Opus, 2-3x faster
  • Sonnet: Balanced cost/quality for most tasks
  • Opus: Highest quality for complex/security-critical tasks

Quality Scores

Training examples include quality scores (0.0-1.0) based on:

  1. Template Quality (0.80-0.96)

    • Hand-crafted seed templates: 0.90-0.96
    • Paraphrased examples: 0.85-0.90
    • Domain transferred: 0.80-0.85

  2. Category Appropriateness

    • Security tasks: 0.90-0.96 (critical quality)
    • Architecture tasks: 0.85-0.93 (high quality)
    • Code generation: 0.83-0.90 (good quality)
    • Research tasks: 0.80-0.89 (adequate quality)
    • Review tasks: 0.82-0.90 (good quality)

Integration with RuvLTRA

Fine-Tuning Pipeline

use ruvllm::training::DatasetGenerator;
use ruvllm::SonaLlm;

// 1. Generate dataset
let dataset = DatasetGenerator::new(config).generate();

// 2. Split data
let (train, val, _test) = dataset.split(0.7, 0.15, 0.15, 42);

// 3. Fine-tune model
let model = SonaLlm::new(config)?;
for example in train {
    let embedding = model.embed(&example.input)?;
    let target = encode_agent(&example.output_agent);
    model.train(embedding, target)?;
}

Model Architecture

The dataset supports training multiple heads:

  1. Task Embedding Layer

    • Input: Task description + context
    • Output: 768-dim semantic embedding

  2. Agent Classification Head

    • Input: Task embedding
    • Output: 5-way softmax (5 agent types)

  3. Model Selection Head

    • Input: Task embedding + complexity features
    • Output: 3-way softmax (Haiku/Sonnet/Opus)

  4. Quality Prediction Head

    • Input: Task embedding
    • Output: Regression (0-1 quality score)

Domain Types

The dataset covers 8 technical domains:

  • Web: Frontend, backend, full-stack development
  • Systems: Operating systems, low-level programming
  • DataScience: ML, analytics, data processing
  • Mobile: iOS, Android, cross-platform
  • DevOps: Infrastructure, CI/CD, deployment
  • Security: Cryptography, vulnerabilities, compliance
  • Database: SQL, NoSQL, data modeling
  • Api: REST, GraphQL, API design

Template System

The generator uses 100+ hand-crafted templates per category:

TaskTemplate {
    input: "Implement a {function_type} function in {language}",
    context: "Should {requirements} and optimize for {target}",
    complexity: ComplexityLevel::Moderate,
    domain: DomainType::Web,
    tags: vec!["code-generation", "function"],
    quality: 0.87,
}

Placeholders are filled with random values:

  • {language}: Rust, TypeScript, Python, Go, Java
  • {framework}: React, Vue, Angular, Svelte
  • {function_type}: async, recursive, higher-order
  • {data_structure}: binary tree, hash map, linked list

Running the Examples

Complete SOTA Training Pipeline

# 1. Generate 500+ Claude-powered hard negatives
node npm/packages/ruvllm/scripts/training/claude-hard-negatives.js --count=50

# 2. Merge all triplets (base + hard negatives)
cat ~/.ruvllm/training/ruvltra-finetuned/triplets.jsonl > combined.jsonl
echo "" >> combined.jsonl
cat ~/.ruvllm/training/claude-hard-negatives.jsonl >> combined.jsonl
echo "" >> combined.jsonl
cat ~/.ruvllm/training/claude-hard-negatives-batch2.jsonl >> combined.jsonl

# 3. Run REAL contrastive training with Candle (30 epochs)
cargo run --example train_real --release --features candle -- \
    --triplets ~/.ruvllm/training/combined-sota.jsonl \
    --base-model ruvltra-claude-code-0.5b-q4_k_m.gguf \
    --output ruvltra-claude-code-sota.gguf \
    --epochs 30 \
    --grpo  # Enable GRPO feedback loop

# 4. Merge trained adapter with base model
bash ruvltra-claude-code-sota.gguf.weights/merge_adapter.sh

# 5. Benchmark the improvement
node npm/packages/ruvllm/scripts/hybrid-model-compare.js

Simulated Contrastive Fine-Tuning (Quick Test)

# Simulated training (no real weight updates, for testing)
cargo run --example train_contrastive --release -- \
    --triplets ~/.ruvllm/training/combined-sota.jsonl \
    --epochs 30

# Expected output:
# - 88%+ embedding-only accuracy
# - 81%+ hard negative accuracy
# - 100% hybrid routing accuracy

Dataset Generation

# Generate dataset
cargo run --example generate_claude_dataset --release

# Output files:
# - claude_training_full.jsonl (all examples)
# - claude_training_train.jsonl (70% training)
# - claude_training_val.jsonl (15% validation)
# - claude_training_test.jsonl (15% test)
# - claude_training_stats.json (statistics)

Testing

# Run tests
cargo test --package ruvllm --lib training

# Test specific functionality
cargo test --package ruvllm test_dataset_generation
cargo test --package ruvllm test_dataset_augmentation
cargo test --package ruvllm test_model_recommendation

Performance

Dataset generation is highly optimized:

  • Generation Speed: ~10,000 examples/second
  • Memory Usage: ~200 MB for 3,000 examples
  • Export Speed:
    • JSONL: ~50 MB/s
    • JSON: ~30 MB/s (pretty-printed)

Future Enhancements

Planned Features

  • Parquet export format
  • HuggingFace Datasets integration
  • Multi-language support (non-English tasks)
  • Custom template loading
  • Active learning integration
  • Difficulty progression scheduling
  • Cross-validation splits
  • Balanced sampling strategies

Research Directions

  • Few-shot learning examples
  • Task decomposition datasets
  • Multi-turn conversation datasets
  • Code execution feedback datasets
  • Self-improvement trajectory datasets

References

  • Claude Flow: https://github.com/ruvnet/claude-flow
  • RuvLTRA Architecture: ../../README.md
  • SONA Learning: ../../../sona/README.md
  • Dataset Format: ../../../../docs/claude_dataset_format.md

License

MIT OR Apache-2.0