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- SMITH unifies tool creation and use for LLM agents
The new SMITH methodology unifies tool generation and invocation training within a single policy, employing three independent reward axes to correct errors in schema, code, and outcome. Testing was conducted on a 4 billion parameter Qwen3 model.
- Field
- training language models
- What they did
- Researchers proposed the SMITH method, which simultaneously trains a language model to create tools and use them by combining these tasks into a single system.
- Why it matters
- This allows the model to receive feedback on errors in code schema, generated code, and execution results, making it more effective at working with tools.
#llm#tool use#reinforcement learning#qwen3#schema grounding
