Safety
Paper Instruments benchmarked sandbox providers for their RL rollouts, and E2B's tool execution came in up to 3x faster than every other san…
Paper Instruments benchmarked sandbox providers for their RL rollouts, and E2B's tool execution came in up to 3x faster than every other sandbox provider they tested, reducing idle GPU time and traini
Paper Instruments benchmarked sandbox providers for their RL rollouts, and E2B's tool execution came in up to 3x faster than every other sandbox provider they tested, reducing idle GPU time and training cost. @paperinstr trains frontier models for knowledge work (consulting, finance, banking, law), running thousands of concurrent GRPO rollouts, each needing the solver and grader fully isolated from one another to prevent reward hacking. Every rollout also needs to start from the exact same state in order to reflect the policy being trained accurately. It also needs to boot and execute fast, since the GPU sits idle waiting on the rollout to finish. With @e2b, Paper Instruments isolates each rollout in a microVM, builds the initial state from a template, then snapshots the running sandbox, letting thousands of rollouts launch straight from the snapshot instead of booting cold, so they get consistency and speed while reducing training cost. Read the full case study: http://e2b.dev/blog/paperinstruments Media
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Source: Yohei Nakajima (X) | 2026-09-03