New research from Microsoft.

This one is on training computer-use agents at scale.

Recent pipelines generate synthetic environments in bulk， which moved the bottleneck from how many exist to what is inside each one. Echoverse compiles specifications into stateful applications whose tasks are graded against the application's own database， then runs a co-evolution loop that reads every graded rollout twice. Once as repairs to the environment， its tasks and its verifier. Once as training signal for the model.

On the same domains， shallow environments pushed live-site accuracy below the base model， from 80.0 down to 75.0. Deep ones raised it， 80.0 to 85.0 and 48.0 to 65.0.

Repairing a single environment lifted the model trained on it from 16.2% to 38.5%. Across twelve environments， a 9B model went from 36.5% to 67.1% on fourteen evaluation splits， within fourteen points of the much larger frontier model that taught it.

They release four environments as a benchmark with applications， seed data and grounded graders.

Paper： https://arxiv.org/abs/2607.28074

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