Reproducible RL environments

boxd proposes improving reproducibility and speed in reinforcement learning by forking a fully initialized microVM rather than repeatedly rebuilding and reseeding environments. Because forks begin from identical memory, disk, processes, loaded weights, simulator state, and caches, they provide isolated warm-start rollouts that can be created rapidly and run in parallel, while reducing dependence on coordinating random seeds across every dependency. Developers still must control policy randomness and external nondeterminism such as clock reads, network responses, machine-specific identities, and nondeterministic accelerator operations, since forking does not make code itself deterministic. The approach supports forks for parallel copies of a baseline, checkpoints for restoring one machine to an earlier state, and versioned snapshots for preserving a reusable baseline across machines or training runs. It is presented as most useful for costly, stateful environments, while simpler environments with little setup may not justify the added infrastructure.

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