New impressive work from Microsoft and collaborators.
And it's one of my favorite recent topics: RL environments.
They show that Qwen3.6-35B-A3B goes from 41.1 to 53.3 on Terminal-Bench 2.1 by training on environments it designed itself.
AutoEnvScaling gives environment design to a proposer agent that works in a terminal, looks at the rollouts of the model being trained, and builds new RL environments from them. The trained model's next rollouts guide the next round.
Building environments in a terminal raises the share of valid environments up to 25 times over prompt-only generation, at up to 75% lower cost per valid environment.
With GPT-5.6-sol or Claude Opus 5 as the proposer, Qwen3.5-35B-A3B scores 4.3 and 3.8 points above matched static environment pools. The gain shrinks when the proposer cannot see solver feedback.
The pipeline also produces environments for software engineering, computer use, and GPU kernel work.
https://arxiv.org/abs/2610.14163
Chat with Paper: https://academy.dair.ai/papers/autoenvscaling-automating-the-data-flywheel-with-terminal-agents-2610.14163