O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruning
This work addresses the challenge of inefficient inference in reasoning LLMs, which is critical for real-world applications, though it appears incremental as it builds on existing fine-tuning techniques.
The paper tackles the problem of high inference time in long-thought reasoning LLMs like OpenAI's O1 by proposing O1-Pruner, a fine-tuning method that reduces reasoning overhead while maintaining or improving accuracy, achieving significant reductions in inference overhead and higher accuracy on mathematical reasoning benchmarks.
Recently, long-thought reasoning LLMs, such as OpenAI's O1, adopt extended reasoning processes similar to how humans ponder over complex problems. This reasoning paradigm significantly enhances the model's problem-solving abilities and has achieved promising results. However, long-thought reasoning process leads to a substantial increase in inference time. A pressing challenge is reducing the inference overhead of long-thought LLMs while ensuring accuracy. In this paper, we experimentally demonstrate that long-thought reasoning models struggle to effectively allocate token budgets based on problem difficulty and reasoning redundancies. To address this, we propose Length-Harmonizing Fine-Tuning (O1-Pruner), aiming at minimizing reasoning overhead while maintaining accuracy. This effective fine-tuning method first estimates the LLM's baseline performance through pre-sampling and then uses RL-style fine-tuning to encourage the model to generate shorter reasoning processes under accuracy constraints. This allows the model to achieve efficient reasoning with lower redundancy while maintaining accuracy. Experiments on various mathematical reasoning benchmarks show that O1-Pruner not only significantly reduces inference overhead but also achieves higher accuracy, providing a novel and promising solution to this challenge. Our code is coming soon at https://github.com/StarDewXXX/O1-Pruner