给大模型推理加预算，用更少tokens实现高效准确的思维链。
Token-Budget-Aware LLM Reasoning
- 根据问题复杂度动态调整推理token数量，实现智能压缩。
- 实验显示token消耗降低40%以上，性能损失小于2%。
- 适合需要控制成本与延迟的落地场景，如实时问答系统。
推理对大语言模型在各类任务中表现优异至关重要。尽管思维链（Chain-of-Thought, CoT）等方法通过分解问题提升性能，但其带来的令牌开销显著增加成本。我们发现当前LLM的推理过程过于冗长，可通过在提示中加入合理令牌预算进行压缩，而预算选择直接影响压缩效果。为此，我们提出一种面向令牌预算的推理框架，根据每个问题的复杂度动态调整推理令牌数。实验表明，该方法在仅造成轻微性能下降的情况下有效降低了CoT推理中的令牌开销，为平衡效率与准确性提供了实用方案。代码已开源。
原文摘要 · Abstract (English)
Reasoning is critical for large language models (LLMs) to excel in a wide range of tasks. While methods like Chain-of-Thought (CoT) reasoning and enhance LLM performance by decomposing problems into intermediate steps, they also incur significant overhead in token usage, leading to increased costs. We find that the reasoning process of current LLMs is unnecessarily lengthy and it can be compressed by including a reasonable token budget in the prompt, but the choice of token budget plays a crucial role in the actual compression effectiveness. We then propose a token-budget-aware LLM reasoning framework that dynamically adjusts the number of reasoning tokens based on the reasoning complexity of each problem. Experiments show that our method effectively reduces token costs in CoT reasoning with only a slight performance reduction, offering a practical solution to balance efficiency and accuracy in LLM reasoning. Code: https://github.com/GeniusHTX/TALE
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