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FunReason-MT is presented, a novel data synthesis framework for real-world multi-turn tool use that resolves the complexity barrier in multi-turn FC data by employing 1) Environment-API Graph Interactions to gather varied high-quality trajectories, 2) Advanced Tool-Query Synthesis to simplify hard query construction, and 3) Guided Iterative Chain for sophisticated CoT generation.
LLMs still struggle to generate high-quality interactive HTML applications, despite their advancements in code generation, highlighting a gap that MiniAppBench aims to address.
Forget random sampling – this framework crafts targeted, multi-turn function-calling data that catapults smaller LLMs to state-of-the-art performance.
Forget synthetic data and overfitting: Environment Tuning lets LLM agents learn complex tool-use behaviors directly from the environment, slashing data needs and boosting generalization.
