EREAL: Enhancing Mathematical Reasoning through Error-Aware Learning in Large Language Models

Official implementation of EREAL (Enhancing Reasoning through Error-Aware Learning), a novel framework that enables LLMs to learn from token-level mistakes for improved mathematical reasoning.

📖 Overview

EREAL addresses the limitations of existing error-correction methods by introducing:

• Minimal-edit Correction: Revises only erroneous segments while preserving valid reasoning steps
• Dynamic Error-Aware Loss: Adaptively weights tokens based on error proportion during training
• Fine-grained Supervision: Enables token-level learning from mistakes

🔬 Key Insight: Most model errors require only localized corrections, making coarse-grained rewriting inefficient and disruptive to valid reasoning.

🚀 Quick Start

Installation

cd EREAL
pip install -r requirements.txt

Training

To train a model using EREAL:

# Edit the paths in finetune.sh first
# - Set OUTPUT_DIR to your desired output directory
# - Set DATA_PATH to your training data
# - Set model_name_or_path to your base model path
bash ./script/finetune.sh

Evaluation

To evaluate your fine-tuned model:

# First merge the LoRA weights with the base model
# Edit the paths in evaluate.sh:
# - Set base-model to your base model path
# - Set lora-path to your checkpoint directory
# - Set save-path to where you want to save the merged model

# Then run the evaluation script
bash ./script/evaluate.sh

📊 Supported Models & Benchmarks

Tested Models

• LLaMA-3.1-8B-Instruct
• DeepSeek-Math-7B-Instruct
• Mistral-7B-Instruct-v0.3

Evaluation Benchmarks

Category	Dataset	Samples
In-Distribution	GSM8K	1,319
	MATH	5,000
Out-of-Distribution	ASDiv	2,215
	MAWPS	2,065
	SVAMP	1,000
	TabMWP	1,000
	CARP_EN	976