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Thus, the MNPO update inherits the regret guarantees of OMD: the average regret after T rounds scales as O(1/ √ T), ensuring convergence to equilibrium in the no-regret learning sense · 1999

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cs.AI 1

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2025 1

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Multiplayer Nash Preference Optimization

cs.AI · 2025-09-27 · unverdicted · novelty 6.0

MNPO extends NLHF to multiplayer Nash games, inheriting equilibrium guarantees while showing empirical gains on instruction-following benchmarks under diverse preferences.

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  • Multiplayer Nash Preference Optimization cs.AI · 2025-09-27 · unverdicted · none · ref 48

    MNPO extends NLHF to multiplayer Nash games, inheriting equilibrium guarantees while showing empirical gains on instruction-following benchmarks under diverse preferences.