TokenRouter is a system designed for efficient token-level routing of large language model (LLM) inference, offering higher decoding throughput compared to existing systems. It uses a request-centric programming approach and a delayed-batching scheduler to improve performance.
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TokenRouter improves the efficiency of serving token-level LLM inference by using a request-centric approach and optimized scheduling, making it suitable for applications requiring high throughput.
The paper introduces Trace2Env, a learning-free framework that uses historical interaction traces to create a reusable environment worldbook for simulating realistic environments without rebuilding the original system. It improves next-observation fidelity and long-horizon interaction consistency compared to conventional prompt-based language world models.
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