Rejection Sampling Recipes

Reproducible recipes for rejection sampling in LLM/VLM data synthesis.

Why This Repo

• Easy to run: Pick a recipe and run it; no extra scaffolding.
• Ready-to-use recipes: Text + multimodal flows with answer parsing, a solid judge prompt, and safe image-resize fallbacks.
• Scales when data grows: Ray Data based pipeline, which gives streaming-style processing, batching, concurrency, and checkpoint/resume out of the box.

Core Concepts

Concept	Description
Stage	A single processing step (e.g., sampling, verification, formatting)
Recipe	A sequence of stages that defines a complete data processing workflow
Pipeline	The execution engine that runs recipes with batching, error handling, and checkpoint/resume

Project Structure

rejection-sampling-recipes/
├── src/                          # Core framework
│   ├── base.py                   # Stage and BaseRecipe base classes
│   ├── pipeline.py               # Pipeline execution engine
│   └── utils/                    # Data I/O utilities
├── recipes/                      # Recipe implementations
│   ├── text_sft_simple/          # Text-only recipe
│   ├── vl_cot_sft_plus_parse/    # Text + image recipe (with answer parsing)
│   ├── ifbench/                  # Instruction-following recipe
│   └── cpu_task_demo/            # CPU-intensive demo recipe
├── scripts/                      # Utility scripts
└── tests/                        # Test files and mock data

Installation

Prerequisites

• Python 3.10 or higher
• (Optional) uv for faster dependency management

Option A: Using uv (Recommended)

uv sync

Option B: Using pip

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -r requirements.txt

Development Installation

# Install with development dependencies
uv sync --extra dev

# Or with pip
pip install -e ".[dev]"

Quick Start

All recipes read JSONL in OpenAI-style messages format. Use tests/mock/*.jsonl for concrete input examples.

Available Recipes

1. text_sft_simple (Text-only)

A simple text-only recipe for basic SFT data processing.

bash recipes/text_sft_simple/entrypoint/run.sh

2. vl_cot_sft_plus_parse (Text + Image + Answer Parsing)

A multimodal recipe with Chain-of-Thought and answer parsing.

# Step 1: Add absolute image paths ("abs_path")
python scripts/preprocess_images.py \
  --input tests/mock/text-pic.jsonl \
  --image-base-path /abs/path/to/images \
  --abs-image-path-field abs_path

# Step 2: Run the recipe
bash recipes/vl_cot_sft_plus_parse/entrypoint/run-30b/run-30b.sh

3. ifbench (Instruction Following)

Instruction-following rolling pipeline with feasibility filtering and rule-based checks.

Run the one-time NLTK data setup first: recipes/ifbench/README.md.

bash recipes/ifbench/entrypoint/run.sh

4. cpu_task_demo (CPU-intensive Demo)

A minimal recipe for CPU-heavy processing (prime counting) to verify distributed CPU execution.

bash recipes/cpu_task_demo/entrypoint/run.sh

For multi-node scaling comparison (recommended):

bash recipes/cpu_task_demo/entrypoint/benchmark.sh

Model Service Setup

See scripts/launch_serve/README.md for model service setup and launch steps.

Note: The default launch scripts include ray stop. If you need to run multiple scripts, use separate machines or remove ray stop.

Logging

Environment Variable	Default	Description
LOG_DIR	logs/	Log directory (falls back to /tmp/rejection-sampling-recipes-logs/)
LOG_MAX_BYTES	10MB	Maximum log file size
LOG_BACKUP_COUNT	5	Number of backup log files
LOG_FILE_LEVEL	DEBUG	File logging level
LOG_CONSOLE_LEVEL	INFO	Console logging level

Log files:

• pipeline.log - Driver logs
• pipeline_worker_<pid>.log - Ray worker logs

Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Workflow

# Install dev dependencies
uv sync --extra dev

# Run linting
uvx ruff check .

# Run formatting
uvx ruff format .

# Run tests
pytest

License

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

Citation

If you use this project in your research, please consider citing:

@software{rejection_sampling_recipes,
  title = {Rejection Sampling Recipes},
  author = {guox18},
  year = {2024},
  url = {https://github.com/guox18/rejection-sampling-recipes}
}