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Data Scaling Strategy - Stack 2.9

Target: 50K+ Training Examples

Current State

  • Synthetic examples: 213
  • Code-comment pairs: 4,045
  • Advanced patterns: 306
  • Total estimated: ~5,000-6,000 examples

Target: 50,000+ examples

Scaling Plan

1. Mine OpenClaw Session Logs (10K examples)

Where to look:

  • ~/.openclaw/sessions/ - OpenClaw session logs
  • ~/.claude/sessions/ - Claude Code sessions (if exists)
  • ~/.anthropic/ - Anthropic Claude logs
  • Any custom session history in project directories

Format: Likely JSON, JSONL, or Markdown What to extract:

  • Full conversations with tool use
  • User prompts + assistant responses + tool calls + tool results
  • Multi-turn dialogues
  • Error recovery patterns
  • Different tool combinations

Expected yield: 5,000-15,000 examples depending on usage history.

2. Synthetic Data with Template-Based Generation (20K examples)

Approach: Create hundreds of templates for each tool pattern and generate variations.

For each of 37 tools:

  • Create 10-20 scenario templates (e.g., for FileReadTool: "Read file X", "Show me Y", "What's in Z?")
  • Generate 200-500 variations by:
    • Changing file names, function names, variables
    • Varying parameter values
    • Changing phrasing (synonyms, active/passive, question/command)
    • Adding noise (typos, extra spaces, filler words)
    • Combining multiple tool calls in sequence

Total: 37 tools × 500 variations = 18,500 examples

Tools with highest priority:

  • FileReadTool, FileWriteTool, GlobTool, GrepTool (common)
  • BashTool, TaskCreateTool, Agent-related tools (complex workflows)
  • MCPTool (extension patterns)

3. Public Dataset Integration (20K examples)

Datasets to download (Hugging Face - free):

a) OpenAssistant (oasst1)

  • Conversations from OpenAssistant project
  • Filter: coding-related threads
  • Transform: Convert to tool-use format (synthesize tool calls from intent)
  • Estimated: 5,000 examples

b) CodeAct

  • Already has executable code actions
  • Direct mapping to our tools
  • Estimated: 10,000 examples

c) CodeContests

  • Competition problems + solutions
  • Format as code generation tasks
  • Filter permissive licenses only
  • Estimated: 3,000 examples

d) StarCoder Data (permissive subset)

  • Various code tasks
  • Estimated: 2,000 examples

Total: ~20,000 examples

4. Code-Pair Expansion (10K+ additional)

Already have 4,045 code-comment pairs from src/.

Additional extraction:

  • Parse ALL TypeScript/JS files in src/ more thoroughly
  • Include:
    • Function + JSDoc
    • Class + class comment
    • Interface + description
    • Error handlers
    • Complex algorithms with inline comments
    • Test cases + implementation
  • Target: 10,000 additional pairs

Method:

  • Enhanced parser that finds all code blocks with preceding comment
  • Use local NLP (if needed) to generate comments for code without them
  • Filter for meaningful pairs (>3 lines code, substantive comment)

5. Data Augmentation (5K examples)

From existing high-quality examples:

  • Paraphrase user prompts (local NLP tools)
  • Swap tools in similar contexts (e.g., FileRead → Glob)
  • Add/remove context information
  • Create "failed tool" scenarios with recovery
  • Vary complexity levels

Target: 5,000 augmented examples

Total Estimate

  • OpenClaw logs: 10K
  • Synthetic templates: 20K
  • Public datasets: 20K
  • Code-pairs: 10K
  • Augmentation: 5K
  • Total: ~65,000 examples (exceeds 50K target)

Implementation Steps

  1. Session log mining script - scripts/mine_sessions.py
  2. Synthetic data generator - scripts/generate_synthetic.py
  3. Public dataset downloader - scripts/download_datasets.py
  4. Code-pair extractor - scripts/extract_code_pairs.py
  5. Data augmenter - scripts/augment_data.py
  6. Quality filter - scripts/quality_filter.py
  7. Dataset combiner - scripts/combine_datasets.py

All scripts save to training-data/scaled/ with source tracking.

Quality Control

  • All examples validated against tool schemas
  • Deduplication (exact and near-duplicate)
  • Minimum quality thresholds
  • Balance across tools and complexity
  • 80/10/10 train/val/test split

Timeline (Manual)

Day 1: Session mining + code-pair extraction Day 2: Synthetic generation + public dataset integration Day 3: Augmentation + quality filtering + combining

We can produce 50K+ examples within a few days of focused work.

Status: Ready to implement step 1 (session mining) now.