PromptCoT & PromptCoT-Mamba: Advancing the Frontiers of Reasoning

News

• May 30, 2025: PromptCoT-Mamba released! Introducing an attention-free foundation model for reasoning tasks.
• Apr 11, 2025: PromptCoT-QwQ-32B model and its training data released, achieving new state-of-the-art results.
• Mar 7, 2025: PromptCoT project launched, including the problem generation model, distilled models (PromptCoT-DS series), and associated datasets.

Overview

This repository unifies two synergistic projects aimed at advancing the frontiers of mathematical and code reasoning in Large Language Models (LLMs): PromptCoT and PromptCoT-Mamba.

PromptCoT (Synthesizing Olympiad-Level Problems for Mathematical Reasoning in Large Language Models) addresses the critical challenge of acquiring high-quality, complex problems for training advanced LLMs. It introduces a novel methodology to systematically generate Olympiad-level mathematical problems by modeling the rationale behind expert problem design. This approach not only enhances problem diversity and difficulty but also ensures logical consistency in problem construction, providing a scalable solution for creating robust training datasets.

PromptCoT-Mamba (Scaling Reasoning without Attention) leverages the problem generation capabilities of the PromptCoT pipeline to train PromptCoT-Mamba-7B, the first attention-free foundation model based on the Mamba-2 architecture. This model demonstrates that structured training curricula can enable attention-free models to surpass strong Transformer baselines on a wide array of competition-level math and code reasoning tasks, all while maintaining constant-memory inference without KV caching.

Together, these projects offer a powerful suite of tools, models, and datasets for researchers and developers working on the cutting edge of AI reasoning.

Highlights & Key Results

1. PromptCoT: Problem Generation & Distilled Models

• ✨ The Missing Piece for Test-Time Scaling: A lightweight yet powerful problem generation model enabling the construction of prompt sets at any scale with sufficient quality, perfect for SFT or RL post-training.
• 📖 A Fully Open Project: All models (generation, distilled LLMs) and datasets (generation inputs, SFT data) are open-sourced.
• 🏆 Superior Performance of Distilled Models:
  • PromptCoT-DS-7B consistently surpasses its base model, DeepSeek-R1-Distill-Qwen-7B, with significant gains:
    • +0.9% on MATH-500 (93.7%)
    • +3.2% on AIME2024 (58.7%)
    • +9.2% on AIME2025 (49.2%)
  • PromptCoT-DS-7B (7B parameters) achieves results comparable to larger 32B models like S1-32B and LIMO-32B.
  • PromptCoT-QwQ-32B sets a new standard, outperforming other 32B models by a significant margin:
    • MATH-500: 96.7% ± 0.5%
    • AIME2024: 83.8% ± 2.8%
    • AIME2025: 75.4% ± 4.7%
  • PromptCoT-DS-1.5B demonstrates competitive performance against RL-based models purely through distillation.
• ⚡ Efficiency Without Compromise: PromptCoT-DS-1.5B achieves 40+% AIME scores using over 15× fewer A100 GPU hours compared to models like DeepScaleR-1.5B-Preview.

2. PromptCoT-Mamba: Attention-Free Reasoning

• 🚀 First Attention-Free SOTA: PromptCoT-Mamba-7B is the first attention-free model (Mamba-2 architecture) to outperform strong Transformer baselines in math and code reasoning.
• 🧠 Trained with PromptCoT Pipeline: Utilizes a structured, two-stage curriculum with data generated by PromptCoT.
• 💪 Strong General Performance: PromptCoT-Mamba-7B consistently outperforms 7B-scale Transformer and hybrid Mamba-Transformer baselines.
  • MATH-500: 84.6%
  • AIME 2024: 35.2%
  • AIME 2025: 24.6%
  • Livecodebench: 29.9%
• 🎯 Math Specialization: The math-specialized variant, PromptCoT-Mamba-Math-7B, further boosts math performance:
  • MATH-500: 88.0%
  • AIME 2024: 42.9% (+7.7% over generalist)
  • AIME 2025: 30.8% (+6.2% over generalist)
• ⚡ Inference Efficiency: Offers substantial speedups (e.g., 3.66× faster on 24GB GPU for long sequences) and constant-memory inference, ideal for cost-sensitive or long-context workloads.

Performance Details

PromptCoT Series Performance

PromptCoT-Mamba Performance

General Performance:

Math Specialization vs. Generalist:

Citation

If you find PromptCoT or PromptCoT-Mamba useful in your research, please consider citing the respective papers:

For PromptCoT:

@article{zhao2025promptcot,
  author    = {Zhao, Xueliang and Wu, Wei and Guan, Jian and Kong, Lingpeng},
  title     = {PromptCoT: Synthesizing Olympiad-Level Problems for Mathematical Reasoning in Large Language Models},
  year      = {2025},
  journal   = {arXiv preprint arXiv:2503.02324},
  url       = {http://arxiv.org/abs/2503.02324}
}

For PromptCoT-Mamba:

@article{zhao2025scaling,
  author    = {Xueliang Zhao and Wei Wu and Lingpeng Kong},
  title     = {Scaling Reasoning without Attention},
  journal   = {arXiv preprint arXiv:2505.22425},
  year      = {2025},
  url       = {https://arxiv.org/abs/2505.22425}
}