This is a page that curates AI-related papers published worldwide. All content here is summarized using Google Gemini and operated on a non-profit basis. Copyright for each paper belongs to the authors and their institutions; please make sure to credit the source when sharing.
This paper presents a length-adaptive policy optimization (LAPO) framework to address the problem of excessive token generation in large-scale inference models. LAPO utilizes a two-stage reinforcement learning process that shifts inference length control from an external constraint to an inherent capability of the model. In the first stage, it discovers a statistical distribution of successful solution lengths to learn natural inference patterns. In the second stage, it leverages these patterns as metacognitive guidance, directly integrating them into the model's inference context to achieve flexibility in inference time. Experimental results on mathematical inference benchmarks demonstrate that LAPO reduces token usage by up to 40.9% and improves accuracy by 2.3%. Analytical results demonstrate that models trained with LAPO can allocate computational resources based on problem complexity, achieving efficient inference without compromising quality.
Takeaways, Limitations
•
Takeaways:
◦
We present a novel framework that can significantly improve the efficiency of large-scale language models.
◦
Empowering metacognitive reasoning capabilities that dynamically allocate computational resources based on problem complexity.
◦
Substantial performance improvements in terms of reduced token usage and improved accuracy.
•
Limitations:
◦
The effectiveness of the LAPO framework is limited to mathematical reasoning benchmarks, and its generalizability to other types of problems requires further study.
◦
Since it is reinforcement learning-based, there is a possibility that significant computational resources will be consumed during the training process.
◦
Further validation of performance and scalability in real-world applications is needed.
