Datasets › lilGym
lilGym
lilGym is a benchmark for language-conditioned reinforcement learning in visual environment based on 2,661 highly-compositional human-written natural language statements grounded in an interactive visual environment. Each statement is paired with multiple start states and reward functions to form thousands of distinct Markov Decision Processes of varying difficulty.
Source: lilGym: Natural Language Visual Reasoning with Reinforcement Learning
Image Source: https://github.com/lil-lab/lilgym
Benchmarks archive 2025-07-28
No leaderboard in the archive resolves to this dataset.
Papers archive 2025-07-28
No paper in the archive has a leaderboard row on this dataset; the archive counts 1 paper for it but never published that list.
Dataset loaders archive 2025-07-28
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Tasks archive 2025-07-28
License archive 2025-07-28
Modalities archive 2025-07-28
Languages archive 2025-07-28
No language tagged.
Variants archive 2025-07-28
- lilGym
1 variant name, as the archive lists them.
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