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Tool calling gets a 4x speed boost without training by exploiting structured schemas and retrieval of past invocations.
LLMs can learn to anticipate their opponents' moves and make better decisions in strategic games by explicitly modeling the other player's behavior during training.
LLMs can reason more effectively and efficiently by internalizing tool knowledge, eliminating the need for external documentation and reducing inference costs.
