Progress-Aware Distillation for Mitigating Stagnation in Small Language Model Agents

A paper titled "Progress-Aware Distillation for Mitigating Stagnation in Small Language Model Agents" proposes progress-aware distillation to address stagnation in small language model agents, targeting the substantial token consumption and inference latency that limit practical deployment of LLM-based agent systems. The work responds to recent efforts focused on improving agent-system efficiency, according to the paper's abstract.

While LLM-based agent systems have demonstrated remarkable proficiency in reasoning and solving complex real-world tasks, they also come with substantial token consumption and inference latency, limiting their practical deployment. To enhance the efficiency of agent systems, recent efforts have focu