René's URL Explorer Experiment

{"@context":"https://schema.org","@type":"Organization","name":"Refresh","url":"https://refresh.dev","logo":"https://refresh.dev/images/logo.png","description":"Refresh builds simulation engines and datasets for training frontier AI models. Partnering with domain experts in software engineering, finance, and healthcare.","foundingDate":"2024","sameAs":["https://github.com/refreshdotdev","https://x.com/refresh_dev","https://www.linkedin.com/company/with-refresh/"],"contactPoint":{"@type":"ContactPoint","email":"contact@refresh.dev","contactType":"sales"}}

{"@context":"https://schema.org","@type":"WebSite","name":"Refresh","url":"https://refresh.dev","description":"Refresh builds simulation engines for coding and computer use, for frontier labs to train the next generation of virtual software engineering collaborators.","publisher":{"@type":"Organization","name":"Refresh","url":"https://refresh.dev"}}

{"@context":"https://schema.org","@type":"FAQPage","mainEntity":[{"@type":"Question","name":"What is a simulation environment?","acceptedAnswer":{"@type":"Answer","text":"A simulation environment is a realistic software world an AI agent can act inside, with the same applications, terminals, and workflows a person would use, instrumented so that success is measured automatically. Refresh builds these environments as both evals, to objectively benchmark how well frontier models perform real computer work, and as training gyms with verifiable rewards that models learn from. A verifiable reward is an objective signal of whether the agent completed a task correctly, and it powers the two main ways to post-train an agent: supervised fine-tuning (SFT) on high-quality example trajectories, and reinforcement learning, where the agent improves by acting in the environment and being scored on the outcome. That signal can come from a deterministic verifier, such as a passing pytest suite or a correctly filled form field, or from a rubric-based verifier that scores the work against explicit criteria for less binary tasks. Every example is built to be checked this way, rather than judged on whether output merely looks correct."}},{"@type":"Question","name":"How can enterprises use simulation environments?","acceptedAnswer":{"@type":"Answer","text":"Enterprises use Refresh simulation environments to turn their own high-value workflows into measurable, repeatable tasks. With success defined by verifiable rewards rather than subjective judgment, teams can evaluate agents on the work that matters to them and then hill climb, iterating on prompts, tools, and models against an objective signal until the agent reliably completes the workflow."}},{"@type":"Question","name":"How do you build environments?","acceptedAnswer":{"@type":"Answer","text":"Refresh builds environments by partnering with expert engineers in our network to source real tasks that break the leading models. We reproduce each failure, codify it into a verifiable task, and confirm a high failure rate across frontier models before it goes into an environment, so every task targets a genuine capability gap rather than something models already handle."}},{"@type":"Question","name":"Who uses Refresh?","acceptedAnswer":{"@type":"Answer","text":"We're lucky to be trusted by frontier AI labs and enterprises evaluating and hill climbing the capabilities of their agents. Labs use Refresh environments and evals to measure and push the frontier of what their models can do, and enterprises use them to turn their own high-value workflows into measurable, trainable tasks."}},{"@type":"Question","name":"How can I work with Refresh?","acceptedAnswer":{"@type":"Answer","text":"Email contact@refresh.dev to request environments or discuss a bespoke dataset, or see our careers page at refresh.dev/careers."}}]}