v0.1.2

Release v0.1.2: High-Quality Tool-Use Data Synthesis Framework 🚀

We are thrilled to introduce Sloop, an advanced synthetic data generation framework designed to produce high-quality, reasoning-intensive tool-use datasets for training Large Language Models (LLMs).

Unlike traditional random generation methods, Sloop introduces a Graph-Based Intelligent Synthesis Strategy to ensure logical coherence and domain relevance in multi-turn dialogues.

🧠 Core Mechanism: Intelligent Tool Chain Generation

Sloop abandons completely random tool combinations in favor of a sophisticated graph-theoretic approach to generate realistic user tasks.

1. Automated Dependency Inference (Graph Construction) 🔗

Sloop automatically analyzes your tool definitions (OpenAI schema) to build a massive Tool Dependency Graph.

• Logic: It infers dependencies by matching "upstream outputs" with "downstream inputs."
• Mechanism: If the required parameter of Tool B (e.g., dish_id) appears in the description of Tool A (e.g., "returns a list of dishes with dish_id"), Sloop establishes a directed edge A -> B.
• Result: This creates a structured map of how tools can naturally interact, ensuring that generated workflows are technically viable.

2. Domain-Sticky Sampling (Sticky Random Walk) 🧭

To generate multi-step tasks, Sloop employs a "Sticky Random Walk" algorithm on the dependency graph.

• Domain Stickiness: The algorithm prefers staying within the same or related domains (e.g., Travel -> Hotel -> Payment) rather than making illogical jumps (e.g., Weather -> Stock Market).
• Scoring System:
  • Strong Relation (1.0): Tools within the same category.
  • Weak Relation (0.7): Tools in logically related categories (e.g., Music & Audio).
  • No Relation (0.3): Unrelated domains.
• Probabilistic Selection: The system selects the next tool based on these scores (80% probability for the best logical fit, 20% for diversity), simulating real-world user behavior that is purposeful yet occasionally non-linear.

3. PDA-Driven Execution 🤖

Once a logical tool chain is sampled, Sloop's Pushdown Automaton (PDA) engine orchestrates a multi-agent simulation (User, Assistant, Service) to generate the actual dialogue, complete with Chain-of-Thought (CoT) reasoning and stateful environment updates.

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✨ Key Features

• 📦 Training-Ready: Outputs data in a flat JSON format ready for SFT (Supervised Fine-Tuning) or RLHF.
• 🧠 Stateful Simulation: Maintains a virtual environment state to prevent hallucinations and ensure data consistency.
• ⚡ Scalable: Capable of handling thousands of tool definitions efficiently.

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Build the next generation of reasoning-capable Agent models with Sloop.