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LLMs can now call tools more efficiently and accurately by representing them as loadable parameters, eliminating the need for lengthy in-context documentation.
LLM agent distillation leads to surprisingly high rates of behavioral mimicry, with some student models exhibiting tool-use habits *more* similar to their teachers than the teacher's own family members.
