PyTorch has high rate of wrong algorithm implementations causing incorrect results

8/10 High

Approximately 12% of PyTorch bugs stem from incorrect algorithm implementations, a rate four times higher than TensorFlow's 3%. This means developers may unknowingly get silently wrong results from core framework operations.

PyTorch
Category
other
Workaround
none
Stage
testing
Freshness
persistent
Scope
framework
Upstream
open
Recurring
Yes
Maintainer
active

Sources

Collection History

Query: “What are the most common pain points with PyTorch for developers in 2025?”4/4/2026

We find a much higher occurrence of bugs caused by wrong implementation of algorithms (12% in PyTorch) than the figures reported in TensorFlow (3%).

Created: 4/4/2026Updated: 4/4/2026