mlsanityData to 7 Oct 2026

LLM Active Alignment: A Nash Equilibrium Perspective

arXiv 2602.06836 ↗First posted 6 Feb 2026cs.AIcited by 0

On the map: LLM alignment / RLHF / preference optimisation → Human values and AI feedback alignment

Abstract (arXiv)

We develop a game-theoretic framework for predicting and steering the behavior of populations of large language models (LLMs) through Nash equilibrium (NE) analysis. To avoid the intractability of equilibrium computation in open-ended text spaces, we model each agent's action as a mixture over human subpopulations. Agents choose actively and strategically which groups to align with, yielding an interpretable and behaviorally substantive policy class. We derive closed-form NE characterizations, adopting standard concave-utility assumptions to enable analytical system-level predictions and give explicit, actionable guidance for shifting alignment targets toward socially desirable outcomes. The method functions as an active alignment layer on top of existing alignment pipelines such as RLHF. In a social-media setting, we show that a population of LLMs, especially reasoning-based models, may exhibit political exclusion, pathologies where some subpopulations are ignored by all LLM agents, which can be avoided by our method, illustrating the promise of applying the method to regulate multi-agent LLM dynamics across domains.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 52 of its 76 references that are in the map. First posted on arXiv 6 Feb 2026.

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52 of 76 references.

Closest ancestors · 3

Same direction, newest first

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  2. 2024Modular Pluralism: Pluralistic Alignment via Multi-LLM Collaboration22 Jun
  3. 2022Constitutional AI: Harmlessness from AI Feedback15 Dec

Other references · 49

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  8. 2023From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Modelscited by 257
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  10. 2018Fairness of Exposure in Rankingscited by 189
  11. 2024BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languagescited by 171
  12. 2023Simulating Opinion Dynamics with Networks of LLM-based Agentscited by 153
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  6. 2024CultureBank: An Online Community-Driven Knowledge Base Towards Culturally Aware Language Technologiescited by 98
  7. 2024Reinforcement Learning for LLM Post-Training: A Surveycited by 91
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  10. 2024A Critical Evaluation of AI Feedback for Aligning Large Language Modelscited by 36
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  19. 2025From Debate to Equilibrium: Belief-Driven Multi-Agent LLM Reasoning via Bayesian Nash Equilibriumcited by 24
  20. 2023A Scalable Neural Network for DSIC Affine Maximizer Auction Designcited by 23
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  37. 2024Nash CoT: Multi-Path Inference with Preference Equilibriumcited by 4

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