Exciting work from NVIDIA.

(bookmark it)

Interesting to see this approach to turn public Agent Skills into RL environments.

Lots of excitement around RL environments so this is a great read.

Skill2Env compiles each Skill into executable terminal tasks.

A Codex planner reads the SKILL.md bundle, researches related public assets and splits the Skill into workflows.

A Codex creator then builds each task with programmatic tests and a behavioral rubric taken from the Skill's own quality criteria.

From about 3.4k crawled Skills, the pipeline produced 7,971 tasks across 13 domains, with software engineering under a quarter of the corpus. Generating them with GPT-5.6 Sol cost over $90k in API usage.

After 300 steps of outcome-only RL, Qwen3.8-27B improved from 49.4% to 54.1% on Terminal-Bench 2.1 and from 33.4% to 37.7% pass@1 on S2EBench, their hand-verified held-out benchmark.

Adding the rubric to the reward gave smaller benchmark gains, 50.1% on Terminal-Bench 2.1. Given the source SKILL.md, a judge preferred the rubric-trained model's trajectories over the base model's on 73.0% of tasks, against 54.5% for the outcome-only model.

Paper: https://github.com/NVlabs/Skill2Env/blob/main/paper/Skill2Env_arXiv.pdf

Chat with Paper: https://academy.dair.ai/papers/reinforcing-agents-with-collective-skills