From Long to Lean: Performance-aware and Adaptive Chain-of-Thought Compression via Multi-round Refinement

📌 Overview

This repository provides the official implementation of MACC, a framework for adaptive multi-round compression of Chain-of-Thought (CoT) reasoning.
MACC dynamically shortens reasoning traces while preserving accuracy, achieving:

• 5.6% higher accuracy vs. state-of-the-art CoT compression baselines
• ~47 tokens shorter CoT on average
• Significant inference-latency reduction

Key idea: Token Elasticity – overly tight token budgets may paradoxically increase output length.
MACC exploits this with progressive multi-round compression and a performance estimation module that predicts final accuracy/length before fine-tuning.

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✨ Features

• Multi-round adaptive compression with automatic stopping criterion
• Performance Estimation Hypothesis: Bayesian regression to predict compressed-CoT accuracy & token cost
• Plug-and-play with various LLMs (e.g., LLaMA-3.1, Qwen2.5, DeepSeek-R1) and compressors (e.g., GPT-4o-mini)

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🛠️ Installation

git clone https://github.com/<your-username>/MACC.git
cd MACC
conda create -n macc python=3.10
conda activate macc
pip install -r requirements.txt

🧠 Citation

If you find this work useful, please cite:

@inproceedings{yan2025macc,
  title     = {From Long to Lean: Performance-aware and Adaptive Chain-of-Thought Compression via Multi-round Refinement},
  author    = {Jianzhi Yan and Le Liu and Youcheng Pan and Shiwei Chen and Zike Yuan and Yang Xiang and Buzhou Tang},
  booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
  year      = {2025}
}