ESTP: A New Framework for Predicting LLM Output Length
A new framework called ESTP (Entropy-and-Semantic Token Pooling) has been developed by researchers to enhance the precision of output length predictions in large language models (LLMs). This innovation, discussed in an arXiv paper (2608.15592), tackles a significant challenge in LLM deployment: the necessity of padding sequences to a predetermined maximum length, which can lead to inefficient resource use and reduced throughput. By forecasting output lengths ahead of time, length-aware scheduling minimizes overhead, particularly beneficial for long-context reasoning and reinforcement learning tasks. Unlike existing techniques that focus mainly on token-wise entropy, ESTP incorporates attention-based importance scores from self-attention weights, thus improving the reliability of length predictions. The lightweight framework aims to optimize the efficiency of LLM serving systems.
Key facts
- ESTP stands for Entropy-and-Semantic Token Pooling.
- The framework combines entropy with attention-based importance scores.
- It addresses the problem of sequence padding in LLM serving.
- Length-aware scheduling can reduce compute waste.
- The method is especially beneficial for long-context reasoning and reinforcement learning.
- Existing entropy-guided methods ignore semantic content differences.
- The paper is available on arXiv with ID 2608.15592.
- The announcement type is 'new'.
Entities
Institutions
- arXiv