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piKL and Diplodocus human-regularized planning family

Contribution grade

Human-compatible multi-agent planning · Human-designed research method or resource

Reference score120,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

piKL anchors search to imitation-learned policies through KL-regularized utilities; DiL-piKL distributes regularization strengths and RL-DiL-piKL trains Diplodocus for human-compatible no-press Diplomacy.

Scientific scope

piKL anchors search to imitation-learned policies through KL-regularized utilities; DiL-piKL distributes regularization strengths and RL-DiL-piKL trains Diplodocus for human-compatible no-press Diplomacy. Related planning and learning extensions are one contribution family, with source-specific bylines. Regularized regret bounds are not universal unregularized Nash guarantees: two-player zero-sum equilibrium statements require their stated assumptions, positive-support anchor/log constants and appropriate regularization. Seven-player tournament strength is empirical, and no-press omits natural-language negotiation.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Related planning and learning extensions are one contribution family, with source-specific bylines. Regularized regret bounds are not universal unregularized Nash guarantees: two-player zero-sum equilibrium statements require their stated assumptions, positive-support anchor/log constants and appropriate regularization. Seven-player tournament strength is empirical, and no-press omits natural-language negotiation.

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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