Understanding R1-Zero-Like Training: A Critical Perspective

TL;DR

A controlled examination of R1-Zero-style reinforcement learning that separates robust training effects from claims that are sensitive to setup, reward design, and evaluation choices.

Why it matters

Reasoning-RL results are easy to overread. This work is useful because it asks which observations reproduce across settings and which depend on implementation details.

Key findings

1. 01

   Training dynamics should be interpreted alongside reward and evaluation design.

2. 02

   Reproducibility requires reporting the complete optimization setup, not only headline benchmark scores.

Scaling dimensions

Models, methods & benchmarks

Algorithms

R1-Zero-style RL

Topics