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NVIDIA's Skill2Env compiles public Agent Skills into 7,971 terminal tasks

DAIR.AI

NVIDIA's Skill2Env compiles each public Agent Skill into executable terminal tasks for reinforcement learning. 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 3,400 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 more than $90,000 in API usage. The write-up is the Skill2Env paper.

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, its hand-verified held-out benchmark. Adding the rubric to the reward produced a smaller benchmark gain, to 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, compared with 54.5% for the outcome-only model.

#skill2env#nvidia#agent-skills#reinforcement-learning#qwen

Only 35.8% of the cleanest public terminal-agent RL collection passed a Salesforce AI Research audit

Salesforce AI Research put that collection at 35.8% clean and two others at 10.1% and 3.3%, after finding reward errors in both directions. The authors say RIVER, which filters defective environments, increases RL gains by 106% on Terminal-Bench-Lite and 30% on Terminal-Bench v2.1 while using fewer than 30% of TMax's environments.