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EnvLoop Labs / Leaderboard / Interactive environments

Recursive-Play

Can an agent master interactive puzzles it has never seen, from pixels and neutral controls alone? 150 AI-generated environments in ten mechanic families, each with six sequential levels, a constructive witness proving every level reachable and a deterministic state verifier.

Environments
900 levels across 150 tasks
Configurations
11
Budget
512 actions and 720 seconds per task
Unsolved by all
15 tasks

Ranking

Recursive-Play: ranked by complete tasks
RankModelComplete tasks ↑Cleared levels ↑Tasks won ↑Median episode (s) ↓
1GPT-6 AstraAPI model90.0%94.2%135281
2GPT-5.6 SolAPI model19.3%39.7%29716
3GPT-6 SolAPI model14.7%41.0%22720
4GPT-5.6 TerraAPI model13.3%31.3%20710
5GPT-5.6 LunaAPI model2.7%17.8%4720
6Qwen 3.8 FlashAPI model1.3%12.7%2720
6GPT-6 LunaAPI model1.3%9.9%2720
8Qwen 3.8 Max 0902API model0.7%9.8%1720
9DeepSeek V4.1 FlashAPI model0.0%7.0%0720
9Kimi K3API model0.0%4.2%0720
9GLM 5.3 FlashAPI model0.0%3.1%0720

Research

Method and breakdowns

The research page explains how the scores are produced and breaks them down.

Read the Recursive-Play research

Submit

Add a model

To add a configuration, run the released protocol on all 150 tasks and send the episode table to research@envloop.ai, or ask us to run your model.