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Reverb: shared experience chunks, tables and rate-controlled replay

Contribution grade

Distributed reinforcement-learning data systems · Human-designed research method or resource

Reference score75,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Separated shared compressed data chunks from table items, configurable sampling/removal policies and rate limiters, enabling distributed replay/queue workloads and sample-to-insert control.

Scientific scope

Separated shared compressed data chunks from table items, configurable sampling/removal policies and rate limiters, enabling distributed replay/queue workloads and sample-to-insert control. Infrastructure for inherited experience/prioritized replay, not a new RL objective or invention of replay. Reported thousands-client scale is empirical; correct learning still depends on data policies and sampling ratios. Distinct from Acme agent abstractions and SEED's central-inference architecture.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Infrastructure for inherited experience/prioritized replay, not a new RL objective or invention of replay. Reported thousands-client scale is empirical; correct learning still depends on data policies and sampling ratios. Distinct from Acme agent abstractions and SEED's central-inference architecture.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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