POLARIS

Description

POLARIS is an environment for evaluating mathematical reasoning capabilities. It contains 53,291 math reasoning problems with 8 difficulty levels (0/8 to 7/8) based on pass rate estimation. The environment uses symbolic comparison for answer verification, handling equivalent expressions.

Capabilities

• Mathematical reasoning across difficulty levels
• Symbolic answer verification
• Equivalent expression handling
• Multi-format numerical answers

Compute Requirements

Agents are given a standard environment with no sandbox or file system access.

License

Apache 2.0.

Tasks

There is one split in this environment:

• train: 53,291 tasks

Tasks span 8 difficulty levels from easy (0/8) to hard (7/8).

Reward Structure

This is a single-turn environment. The agent submits an answer via the answer tool. Verification uses symbolic comparison, handling equivalent expressions (e.g., "1/2" equals "0.5"). Reward is binary: 1.0 if correct, 0.0 if incorrect.

Data

Data consists of a Parquet file (polaris_tasks.parquet, ~11 MB) sourced from HuggingFace POLARIS-Project/Polaris-Dataset-53K. Each row contains a problem statement, expected answer, and difficulty level. Data is stored on the OpenReward platform.

Tools

Time Horizon

Single-turn. The agent reads the math problem and submits one answer.

Environment Difficulty

[Put environment difficulty statistics here]

Other Environment Requirements

No external API keys required.

Safety

Agents in POLARIS solve mathematical problems in a standard environment. The environment does not present direct safety risks.

Citation

@misc{Polaris2025,
  title={POLARIS: A Post-Training Recipe for Scaling Reinforcement Learning on Advanced Reasoning Models},
  url={https://hkunlp.github.io/blog/2025/Polaris},
  author={An, Chenxin and Xie, Zhihui and Li, Xiaonan and Li, Lei and Zhang, Jun and Gong, Shansan and Zhong, Ming and Xu, Jingjing and Qiu, Xipeng and Wang, Mingxuan and Kong, Lingpeng},
  year={2025}
}