muradil211/ToolWeave_stage3
muradil211/ToolWeave_stage3 is a 4 billion parameter language model based on the Qwen3-4B-Instruct family, specifically designed for advanced multi-turn tool-calling capabilities. This model utilizes Boundary-Guided Online Reinforcement Learning, incorporating verified online data synthesis and multi-turn progress rewards. It excels in complex tool-use scenarios by detecting capability boundaries and performing strict execution and semantic validation, making it suitable for developing robust AI agents that interact with external tools.
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ToolWeave Stage 3: Advanced Multi-Turn Tool-Calling
muradil211/ToolWeave_stage3 is the final release in the ToolWeave project's Stage 3, built upon the Qwen3-4B-Instruct base model. This 4 billion parameter model is specifically engineered for multi-turn tool-use learning through a novel approach called Boundary-Guided Online Reinforcement Learning.
Key Capabilities & Features
- Boundary-Guided Learning: Enhances tool-use by detecting the operational boundaries of tools.
- Verified Online Data Synthesis: Generates and validates training data in real-time to improve agent performance.
- Strict Execution & Semantic Validation: Ensures tool calls are both syntactically correct and semantically appropriate for the given context.
- Dynamic Replay: Utilizes past interactions to refine future tool-calling strategies.
- Combined Global/Local Tool-Call Credit: Optimizes reward signals for more effective learning in complex sequences.
- Multi-Turn Progress Reward: A specialized training signal designed to improve performance over extended tool-use dialogues.
Performance
Evaluated on a balanced 400-row held-in set, ToolWeave Stage 3 achieved an overall accuracy of 48.50% in complete-entry BFCL Multi-Turn tasks. This includes specific scores across different categories:
- Base: 56.00%
- Missing Function: 50.00%
- Missing Parameter: 42.00%
- Long Context: 46.00%
When to Use This Model
This model is ideal for developers building AI agents that require sophisticated, reliable, and multi-step interactions with external tools. Its specialized training in boundary detection and verified data synthesis makes it particularly strong for applications where tool-calling accuracy and robustness in complex, multi-turn scenarios are critical.