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Large language model agents solve tasks through long sequences of tool calls, and the prevailing design has a single strong model generate every step, although many steps are routine enough for a much smaller model. Existing ways of sharing this work keep the strong model as the generator: routers and collaborative age...
Large Language Models (LLMs) have shown strong performance on tool-use agentic tasks when given a fixed tool schema. Yet a tool schema is not the action space of an agent; it is merely one interface representation of it. The same executable action can be exposed through many different, functionally equivalent tool defi...
This work studies optimal learning rate (LR) schedules under the functional scaling law (FSL) framework, which decomposes training dynamics into signal learning and noise forgetting, and shows that precise tuning of the decay shape is often unnecessary.
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