ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning
This addresses the computational inefficiency of long-thinking LLMs for users needing faster inference, though it is an incremental improvement over existing early-exit methods.
The paper tackles the problem of inefficient and redundant thinking processes in long-thinking LLMs by introducing ThinkPrune, a reinforcement learning method that prunes thinking length with iterative token limits, resulting in a 50% reduction in reasoning length with only a 2% performance drop on the AIME24 dataset.
We present ThinkPrune, a simple yet effective method for pruning the thinking length for long-thinking LLMs, which has been found to often produce inefficient and redundant thinking processes. Existing preliminary explorations of reducing thinking length primarily focus on forcing the thinking process to early exit, rather than adapting the LLM to optimize and consolidate the thinking process, and therefore the length-performance tradeoff observed so far is sub-optimal. To fill this gap, ThinkPrune offers a simple solution that continuously trains the long-thinking LLMs via reinforcement learning (RL) with an added token limit, beyond which any unfinished thoughts and answers will be discarded, resulting in a zero reward. To further preserve model performance, we introduce an iterative length pruning approach, where multiple rounds of RL are conducted, each with an increasingly more stringent token limit. We observed that ThinkPrune results in a remarkable performance-length tradeoff -- on the AIME24 dataset, the reasoning length of DeepSeek-R1-Distill-Qwen-1.5B can be reduced by half with only 2% drop in performance. We also observed that after pruning, the LLMs can bypass unnecessary steps while keeping the core reasoning process complete. Code is available at https://github.com/UCSB-NLP-Chang/ThinkPrune.