"Chain-of-Reasoning: Towards Unified Mathematical Reasoning in Large Language Models via a Multi-Paradigm Perspective"

LLMs can not only reason but also improve their own training data, leading to better reasoning.

This paper enhances LLM reasoning by refining training datasets with LLM-generated reasoning paradigms, utilizing a universal text template for training.

-----

https://arxiv.org/abs/2501.11110

Original Problem 🤔:

→ LLMs require high quality datasets for effective reasoning.

→ Creating datasets for complex reasoning is challenging and resource intensive.

→ Current datasets may lack diverse and refined reasoning approaches.

-----

Solution in this Paper 💡:

→ The paper introduces a PPT method featuring dataset enhancement.

→ It leverages LLMs to generate and refine reasoning approaches within datasets.

→ Specific prompts guide LLMs in enhancing datasets with structured instructions.

→ A universal text template is applied to standardize all training samples.

→ Training is conducted in three stages, with adjustments to epochs and sequence lengths.

→ DeepSpeed ZeRO Stage 3 and Flash-Attention are used to improve computational efficiency during training.

→ An annealing strategy at the end of training further enhances model accuracy on complex tasks.

-----

Key Insights from this Paper 🧠:

→ LLMs can be effectively utilized to improve the quality and diversity of their own training datasets.

→ Structured prompts are essential for guiding LLM-driven dataset enhancement processes.

→ Universal text templates streamline the processing and consistency of training data.

→ Distributed optimization and attention mechanisms are crucial for efficient LLM training.

-----

Results 📈:

→ Majority vote strategy with 8 samples on GSM8K surpasses GPT-4o's Pass@1 performance of 90.5%.