From ecd45f14cd7a9fc982b2442e49da9dcbf0ec51f3 Mon Sep 17 00:00:00 2001 From: Elijah Beregovsky <53491519+BIGfoot496@users.noreply.github.com> Date: Wed, 3 Jun 2026 10:19:42 +0300 Subject: [PATCH 1/2] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 2ed76db67..eaccb3102 100644 --- a/README.md +++ b/README.md @@ -192,6 +192,7 @@ For information on how to download NLD-AA and NLD-NAO, see the dataset doc [here Otherwise checkout the tutorial Colab notebook [here](https://colab.research.google.com/drive/1GRP15SbOEDjbyhJGMDDb2rXAptRQztUD?usp=sharing). # Papers using the NetHack Learning Environment +- Matthews, et al., [Revisiting The NetHack Learning Environment](https://iclr-blogposts.github.io/2026/blog/2026/revisiting-the-nle/#conclusion), ICLR Blogposts, 2026. - Paglieri et al. [BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games](https://arxiv.org/abs/2411.13543) (UCL, IDEAS NCBR, NYU, Oxford, Anthropic, ICLR 2025) - Klissarov et al. [MaestroMotif: Skill Design from Artificial Intelligence Feedback](https://arxiv.org/abs/2412.08542) (Mila, FAIR, UT Austin, Alberta, Amii, ICLR 2025) - Klissarov et al. [Motif: Intrinsic Motivation from Artificial Intelligence Feedback](https://arxiv.org/abs/2310.00166) (Mila, FAIR, UT Austin, ICLR 2024) From 355805f0a8008f806989f44b4d1113266cefaf20 Mon Sep 17 00:00:00 2001 From: Elijah Beregovsky <53491519+BIGfoot496@users.noreply.github.com> Date: Sat, 6 Jun 2026 20:21:01 +0300 Subject: [PATCH 2/2] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index eaccb3102..200438dd3 100644 --- a/README.md +++ b/README.md @@ -192,7 +192,7 @@ For information on how to download NLD-AA and NLD-NAO, see the dataset doc [here Otherwise checkout the tutorial Colab notebook [here](https://colab.research.google.com/drive/1GRP15SbOEDjbyhJGMDDb2rXAptRQztUD?usp=sharing). # Papers using the NetHack Learning Environment -- Matthews, et al., [Revisiting The NetHack Learning Environment](https://iclr-blogposts.github.io/2026/blog/2026/revisiting-the-nle/#conclusion), ICLR Blogposts, 2026. +- Henaff et al. [Scalable Option Learning in High-Throughput Environments](https://arxiv.org/abs/2509.00338), arXiv:2509.00338 \[cs.LG\]. - Paglieri et al. [BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games](https://arxiv.org/abs/2411.13543) (UCL, IDEAS NCBR, NYU, Oxford, Anthropic, ICLR 2025) - Klissarov et al. [MaestroMotif: Skill Design from Artificial Intelligence Feedback](https://arxiv.org/abs/2412.08542) (Mila, FAIR, UT Austin, Alberta, Amii, ICLR 2025) - Klissarov et al. [Motif: Intrinsic Motivation from Artificial Intelligence Feedback](https://arxiv.org/abs/2310.00166) (Mila, FAIR, UT Austin, ICLR 2024)