Optigami trains an LLM to generate FOLD-format crease patterns that fold into target origami shapes, using GRPO with Unsloth LoRA on Colab. Computational origami design has direct applications in satellite solar panel deployment, self-folding medical stents, drug delivery molecular design, deployable space structures, and soft robotics, this environment teaches models the spatial reasoning needed to bridge 2D crease patterns to 3D folded structures.
Kube SRE Gym is a self-improving RL environment where a small language model (Qwen3-1.7B) learns to diagnose and fix real Kubernetes production incidents…
RL environment for autonomous biologist agents. Simulate an entire biological worldstate based on scientifically-accurate single-cell standards. Naive agents…
ShopRLVE-GYM HF Blog https://huggingface.co/blog/thebajajra/shop-rlve-gym
Our agent uses OpenEnv to build a curriculum of video game environments that train a TinyLlama 1.1B agent via GRPO reinforcement learning. We implement three…
GAIA is an OpenEnv-compatible geospatial reasoning environment where an AI agent infers hidden real-world locations using multi-step tool calls (terrain…
Voyager-VRAM is a long-horizon workplace simulator where LLM agents must navigate a realistic office environment ( emails, Slack, shared drive, spreadsheets…
