AI Research Atlas

Absolute Zero (AZR)

Tsinghua / BIGAI / Penn State · 6 May 2025

A model proposes its own coding tasks and verifies them by execution, improving reasoning with zero external training data.

One model plays both proposer and solver, with a code executor as the verifier. Qwen2.5-7B-Coder gains about 10 points overall with no curated data. Early open evidence for self-play 'zero-data' RL loops.

Date
Tuesday, 6 May 2025
Lab
Tsinghua / BIGAI / Penn State
Kind
paper
Access
open weights

Figures

MeasureValueMeasured by
Overall average gain (7B Coder)+10.2 pts
coding +5.0, math +15.2
authors

arXiv v1 2025-05-06, v3 2025-10-16. Corresponding authors Zilong Zheng, Gao Huang.

Sources

  1. arxiv.org/abs/2505.03335
  2. andrewzh112.github.io/absolute-zero-reasoner/

This record was checked against its sources on 6 October 2026. How we check

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