SelfBudgeter: Adaptive Token Allocation for Efficient LLM Reasoning

Understand

Recently, large reasoning models demonstrate exceptional performance on various tasks.

• However, reasoning models always consume excessive tokens even for simple queries, leading to resource waste and prolonged user latency.
• To address this challenge, we propose SelfBudgeter - a self-adaptive reasoning strategy for efficient and controllable reasoning.
• Specifically, we first train the model to self-estimate the required reasoning budget based on the query.