EvoPolicyGym D
AI benchmark
- Tier
- D why
- Snapshot
What is EvoPolicyGym?
EvoPolicyGym measures how well harness-model agents iteratively edit and improve executable policies in compact interactive RL environments under a fixed interaction budget.
Games mostly
“a benchmark built from compact interactive RL environments that evaluates how agents iteratively improve explored policies”
arxiv.org/abs/2607.02440v1 ↗
Paper: arxiv.org/abs/2607.02440v1 ↗
Tier D
D
Long tail: little or no usage data yet.
How much stands behind a tier: usage, who backs it, a confirmed line from its own site or paper, and a clear read on X. High with three of the four, medium with two, low otherwise.
- 3 models scored
- fewer than 5 models scored
- in curated RL directories (1 pt)
How we got to tier D
- Adoption: 3 models scored; fewer than 5 models scored. Tier D.
- Not yet read on X.
- In curated RL directories: 1 point, a floor of none.
- Confirmed: a line from its own site or paper places it.
A clear view on X moves a tier one step up or down; most entries don't move. How tiers work.
Where EvoPolicyGym sits
Its domain on the map, live. Hover a logo for its name; click to open it. Open it on the full map
Next: browse all benchmarks. Entry from the RL Research daily scrape of public sources, 2026-10-19 snapshot.