E2B sandbox runs RL rollouts up to 3x faster, cutting idle GPU time and training cost
badphilosopher · x · 2026-09-04
- Paper Instruments, which trains frontier models for knowledge work (consulting, finance, law), detailed its RL training stack: each of thousands of concurrent GRPO/CISPO rollouts needs an isolated filesystem and app state, and must boot from identical initial states to keep training signals clean.
- GPUs idle while waiting on rollouts, so tool execution speed directly drives training cost. Instruments benchmarked sandbox providers and found E2B's tool execution up to 3x faster than every other provider tested.
- Rollout data (actions, file changes, rubric-based rewards) flows into the pipeline in structured, traceable form, with solver and grader isolated to prevent reward hacking.
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