The use case: how Paper Instruments deploys E2B
Paper Instruments runs its reinforcement learning rollouts inside E2B sandboxes so each concurrent trajectory has its own isolated file system and state. They also snapshot initialized sandboxes (file system and memory) so rollouts can start quickly from a consistent environment.
In the customer’s words
E2B provides a very good developer experience. They make it easy to spin up sandboxes, easy to snapshot the running state so we can resume quickly for later training runs, and gives us good intelligence into what's happening inside those sandboxes so we can debug during training.Other AI tools used by Paper Instruments
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Other companies using E2B
Customers across industries, with public evidence.
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GroqConfirmedGo straight to the source
Based on E2B’s published case study. We check the public evidence; the results are reported by the vendor.
e2b.dev- Source published
- Sep 3, 2026
- Last checked
- Nov 10, 2026
Open source capture What the story claims, and what we checked
What we compared with the page.
- The figure: 3 times fasterChecked
Printed word for word on the page, near the name of Paper Instruments.
- The passage quoted aboveChecked
Copied word for word from the page, near the name of Paper Instruments.
- The publication dateChecked
Read from the page’s own metadata, never guessed.
- Paper Instruments uses E2BChecked
Confirmed line.
- The result itselfNot checked
We quote it; we did not measure it.
In Paper Instruments' own benchmarking, E2B's tool execution came in up to 3 times faster than other sandbox providers they evaluated. Faster startup and faster command execution both translate directly into less idle GPU time and lower training cost.