Internalizing Self-Consistency in Language Models: Multi-Agent Consensus Alignment

2025-09-18

AI Summary

Language models often produce inconsistent and contradictory responses during reasoning due to unreliable path selection, and existing inference-time methods fail to fundamentally resolve this inconsistency. Method: We propose a multi-agent debate framework that internalizes self-consistency as a learnable model property: multiple agents concurrently generate reasoning paths, and a consensus alignment mechanism—based on majority or minority voting—guides reinforcement learning without external supervision. Our approach builds upon the MACA RL framework, integrating multi-agent negotiation, peer argument dependency, and sampling-based path selection. Results: Experiments demonstrate substantial improvements: +27.6% self-consistency on GSM8K; +23.7% single-agent accuracy on MATH; +42.7% multi-agent decision accuracy on MathQA; and strong generalization to unseen benchmarks such as GPQA.

Technology Category

• Multiagent Systems: Multiagent Learning
• Natural Language Processing: Safety and Robustness
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Language Models (LMs) are inconsistent reasoners, often generating contradictory responses to identical prompts. While inference-time methods can mitigate these inconsistencies, they fail to address the core problem: LMs struggle to reliably select reasoning pathways leading to consistent outcomes under exploratory sampling. To address this, we formalize self-consistency as an intrinsic property of well-aligned reasoning models and introduce Multi-Agent Consensus Alignment (MACA), a reinforcement learning framework that post-trains models to favor reasoning trajectories aligned with their internal consensus using majority/minority outcomes from multi-agent debate. These trajectories emerge from deliberative exchanges where agents ground reasoning in peer arguments, not just aggregation of independent attempts, creating richer consensus signals than single-round majority voting. MACA enables agents to teach themselves to be more decisive and concise, and better leverage peer insights in multi-agent settings without external supervision, driving substantial improvements across self-consistency (+27.6% on GSM8K), single-agent reasoning (+23.7% on MATH), sampling-based inference (+22.4% Pass@20 on MATH), and multi-agent ensemble decision-making (+42.7% on MathQA). These findings, coupled with strong generalization to unseen benchmarks (+16.3% on GPQA, +11.6% on CommonsenseQA), demonstrate robust self-alignment that more reliably unlocks latent reasoning potential of language models.

Problem

• Addressing language models' inconsistency in generating contradictory responses
• Improving reliability of reasoning pathway selection for consistent outcomes
• Enhancing self-consistency through multi-agent consensus alignment framework

Innovation

• Multi-agent reinforcement learning framework for self-consistency
• Post-trains models using majority/minority debate outcomes
• Leverages deliberative exchanges for richer consensus signals