@inproceedings{wu-etal-2023-lilgym,
    title = "lil{G}ym: Natural Language Visual Reasoning with Reinforcement Learning",
    author = "Wu, Anne  and
      Brantley, Kiante  and
      Kojima, Noriyuki  and
      Artzi, Yoav",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.512",
    doi = "10.18653/v1/2023.acl-long.512",
    pages = "9214--9234",
    abstract = "We present lilGym, a new benchmark for language-conditioned reinforcement learning in visual environments. lilGym is based on 2,661 highly-compositional human-written natural language statements grounded in an interactive visual environment. We introduce a new approach for exact reward computation in every possible world state by annotating all statements with executable Python programs. Each statement is paired with multiple start states and reward functions to form thousands of distinct Markov Decision Processes of varying difficulty. We experiment with lilGym with different models and learning regimes. Our results and analysis show that while existing methods are able to achieve non-trivial performance, lilGym forms a challenging open problem. lilGym is available at \url{https://lil.nlp.cornell.edu/lilgym/}.",
}

We present lilGym, a new benchmark for language-conditioned reinforcement learning in visual environments. lilGym is based on 2,661 highly-compositional human-written natural language statements grounded in an interactive visual environment. We introduce a new approach for exact reward computation in every possible world state by annotating all statements with executable Python programs. Each statement is paired with multiple start states and reward functions to form thousands of distinct Markov Decision Processes of varying difficulty. We experiment with lilGym with different models and learning regimes. Our results and analysis show that while existing methods are able to achieve non-trivial performance, lilGym forms a challenging open problem. lilGym is available at \url{https://lil.nlp.cornell.edu/lilgym/}.
Wu, Anne Brantley, Kiante Kojima, Noriyuki Artzi, Yoav
lilGym: Natural Language Visual Reasoning with Reinforcement Learning