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Echoverse: Deep, Evolving Environments for Training Computer-Use Agents at Scale

Research LLM Agents

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Merged summary

TL;DR - Echoverse generates deep, stateful applications and co-evolves their tasks and verifiers with computer-use agents. Training a 9B model on 12 environments improved performance from 36.5% to 67.1% across 14 evaluation splits.

  • Database-grounded grading evaluates actions against actual application state.
  • Deep environments improved live-site accuracy, while shallow ones degraded it below the base model.
  • Targeted interface-control practice transferred to unseen widget families and the open web.
  • Reinforcement learning with grounded and dense per-step rewards raised held-out scores from 58.8% to 68.0%.

Sources (1)

Echoverse: Deep, Evolving Environments for Training Computer-Use Agents at Scale

arXiv cs.AI Yash Pandya, Sahil Gupta, Sarthak Harne, Archana Yadav, Kavyansh Chourasia, Hussein Mozannar, Vibhav Vineet, Sara Abdali, Corby Rosset, Yash Lara, Ahmed Awadallah, Ece Kamar, Akshay Nambi 2026-07-30 arXiv:2607.28074
Public signals Hugging Face upvotes 13
Providers: Hugging Face · Upvotes 13 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-08-31 14:30:08.310085 UTC

TL;DR - Echoverse generates deep, stateful applications and co-evolves their tasks and verifiers with computer-use agents. Training a 9B model on 12 environments improved performance from 36.5% to 67.1% across 14 evaluation splits.

  • Database-grounded grading evaluates actions against actual application state.
  • Deep environments improved live-site accuracy, while shallow ones degraded it below the base model.
  • Targeted interface-control practice transferred to unseen widget families and the open web.
  • Reinforcement learning with grounded and dense per-step rewards raised held-out scores from 58.8% to 68.0%.
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