[NeurIPS 2025] Reasoning Path Compression: Compressing Generation Trajectories for Efficient LLM Reasoning

Official implementation of the NeurIPS 2025 paper "Reasoning Path Compression: Compressing Generation Trajectories for Efficient LLM Reasoning"

RPC is a training-free method for accelerating inference of reasoning language models by leveraging the semantic sparsity of generated reasoning paths. It improves throughput and reduces memory usage with minimal accuracy drop.

🚀 Key Features

• Efficient Inference for Reasoning LLMs
  Speeds up autoregressive decoding by selectively pruning KV cache entries while preserving reasoning quality.

• Training-Free Compression
  Applies directly at inference time without requiring fine-tuning or supervision.

• Semantic-Aware Pruning
  Retains only tokens with high importance, estimated from a small attention-based selector window of recent queries.

• Significant Throughput Gains
  Up to 1.60× faster generation with only 1.2% drop in pass@1 accuracy on the AIME 2024 benchmark.

• Memory Usage Reduction
  Shrinks KV cache size during decoding, enabling longer generations under memory constraints.

Key Results

Usage

1. Install

git clone https://github.com/jiwonsong-dev/ReasoningPathCompression.git
conda create -n rpc python=3.11
conda activate rpc
cd ReasoningPathCompression

# install requirements
pip install -r requirements.txt

# install flash-attn
# We recommend installing flash-attn version suitbale for your environment
# The links can be found at: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.7.4.post1
pip install flash-attn==2.7.4.post1

3. Run

# Run demo with custom input prompt
python -m example

# Run evaluation on reasoning benchmarks
bash scripts/run_aime24.sh
bash scripts/run_livecodebench_v5.sh
bash scripts/run_ifeval.sh

# Run throughput benchmark
bash scripts/benchmark_throughput.sh

Acknowledgements

Our codes for running evaluation and scoring the results are based on QwQ repository.

Citation

If you want find your research relevant to Reasoning Path Compression, please cite our work:

@article{rpc,
  title={Reasoning Path Compression: Compressing Generation Trajectories for Efficient LLM Reasoning},
  author={Jiwon Song, Dongwon Jo, Yulhwa Kim, Jae-Joon Kim},
  journal={arXiv preprint arXiv:2505.13866},
  year={2025}
  }