Tag
PORA 提出了一种配对结果引导的推理分配方法，让 LLM 智能体学习在何种状态与剩余预算下使用长推理更划算，在 BFCL memory 等任务上大幅提升性能并显著节省 token 与模型请求。
A new 30B model matches systems 20-30x its size on popular benchmarks while using up to 95% fewer reasoning tokens than comparable agentic LLMs, achieved through a learned configurator that decides when and how to reason. Model and code are openly available.