DICE: New Framework for Stabilizing Multi-Agent LLM Coordination Through Entropy-Regularized Equilibrium Selection
Researchers introduced DICE, a framework addressing instability in multi-agent large language model systems by formalizing coordination as incomplete-information Markov games and proposing the Heterogeneous Quantal Response Equilibrium (HQRE) concept. Current multi-agent LLM systems often fail to outperform single models with best-of-N sampling due to poor equilibrium selection, causing oscillation and drift between coordination conventions. The work demonstrates improvements of 4.3-8.5 percentage points across reasoning and planning tasks, potentially advancing the reliability of collaborative AI systems.
A new arXiv paper proposes DICE, a framework for stabilizing multi-agent large language model coordination by addressing fundamental equilibrium selection problems. The researchers formalize multi-agent LLM systems as discounted incomplete-information Markov games and identify two key pathologies—oscillation between competing conventions and drift across them—that induce unstable learning and linear Bayesian regret. To resolve this, they introduce the Heterogeneous Quantal Response Equilibrium (HQRE), an entropy-regularized equilibrium concept with agent- and state-dependent temperatures that guarantees uniqueness under monotonicity conditions and admits linearly convergent mirror updates. The framework is instantiated in two algorithms: DICE-PC for coordinating frozen models through prompt-control actions, and DICE-FT for parameter-efficient fine-tuning. Across eleven benchmarks spanning four domains, DICE improves accuracy-cost trade-offs over existing baselines, with particularly strong gains on reasoning and planning tasks.
What's missing
The paper does not discuss computational overhead or wall-clock time comparisons between DICE-PC and DICE-FT relative to baseline approaches. Additionally, the specific nature of the 'four domains' and detailed breakdown of performance across different task types beyond the aggregate improvements would provide fuller context for practitioners.
What different sources said
- arXiv cs.LGCenter
DICE: Entropy-Regularized Equilibrium Selection for Stable Multi-Agent LLM Coordination
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