reasoning-gym/eval

Model Evaluation Framework

A simple asynchronous framework for evaluating language models on reasoning tasks using the OpenRouter API.

Evaluation Results Repository

In order to keep the main repo clean and not clutter it with evaluation traces from different models, we store all evaluation results in a separate repository: reasoning-gym-eval.

If you run evaluations and want to contribute your results, please create a pull request in the reasoning-gym-eval repository, not in the main reasoning-gym repo.

Overview

This framework provides tools to evaluate language models on the reasoning_gym datasets. It supports:

• Concurrent evaluation of multiple questions and datasets
• Customizable dataset configurations
• Automatic result aggregation and summary generation
• Rate limiting for API calls

Setup

1. Set your OpenRouter API key as an environment variable:

export OPENROUTER_API_KEY=your-api-key

2. Prepare your dataset configuration in JSON format (e.g., eval_basic.json):

[
  {
    "name": "dataset_name",
    "parameter1": "value1",
    "parameter2": "value2"
  }
]

Usage

Running Evaluations

You can run evaluations in two ways:

1. Using the provided bash script:

./run_eval.sh

2. Running the Python script directly:

python eval.py --model "model-name" --config "eval_basic.json" --output-dir "results"

Command Line Arguments

• --model: Model identifier (required)
• --config: Path to JSON configuration file (required)
• --output-dir: Directory for saving results (default: "results")
• --max-concurrent: Maximum number of concurrent API calls (default: 10)

Output

The framework generates two types of output files:

1. Detailed results: evaluation_{model}_{timestamp}.json

   • Contains full response data and scoring for each question

2. Summary: summary_{model}_{timestamp}.json

   • Contains aggregated metrics for each dataset

Structure

.
├── eval.py              # Main evaluation script
├── run_eval.sh          # Bash script for running evaluations
├── eval_basic.json      # Dataset configuration file
└── results/             # Output directory (for temporary results)

Contributing Evaluation Results

After running evaluations:

1. Fork the reasoning-gym-eval repository
2. Add your evaluation results to the appropriate directory
3. Create a pull request with your results

This helps us maintain a clean separation between code and evaluation data while collecting comprehensive benchmarks across different models.