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This research introduces Principled Agent Debate (PAD), a multi-agent architecture that reduces sycophancy in RLHF-trained models by arbitrating between agents with opposing philosophical dispositions. The study demonstrates that this approach significantly outperforms single-model and instructed-opposition baselines on accuracy benchmarks, though it identifies pre-training limitations affecting roughly 40% of questions. Future work will likely focus on fine-tuning disposition models to address these pre-training floors and improve overall system robustness.
Principled Agent Debate: Adversarial Arbitration for Sycophancy Reduction in Large Language Models
arXiv:2606.07532v1 Announce Type: new Abstract: RLHF-trained models are systematically biased toward agreement over accuracy, a structural property of the training process. We present Principled Agent Debate (PAD), a multi-agent architecture that mitigates identity-framed sycophancy by arbitrating between two models tuned to opposing philosophical dispositions, with a pragmatist synthesizer evaluating both arguments blind to their origins. This paper evaluates a prompt-based instantiation of PAD. The key mechanisms are static dispositional tuning, identity stripping before synthesis, single-round independent argumentation, and blind arbitration. We evaluate five instantiations on 200 stratified questions from SycophancyEval. All PAD variants (AnCifer, DeWin, FeynStein, BurGal, Trident) significantly outperform the single-model baseline (18.5%) and instructed-opposition baseline (29.0%), with DeWin achieving 48.5% accuracy (z=6.36, p<0.001 versus both). The variants are not significantly different from each other at n=200. The BurGal variant achieves 53.0% but functions as an architectural validity check; its consensus/heterodox axis structurally favors the heterodox model on every benchmark question. A pre-training floor affects an estimated 40% of questions; fine-tuned disposition models are the identified next step.
Abstract:RLHF-trained models are systematically biased toward agreement over accuracy, a structural property of the training process. We present Principled Agent Debate (PAD), a multi-agent architecture that mitigates identity-framed sycophancy by arbitrating between two models tuned to opposing philosophical dispositions, with a pragmatist synthesizer evaluating both arguments blind to their origins. This paper evaluates a prompt-based instantiation of PAD. The key mechanisms are static dispositional tuning, identity stripping before synthesis, single-round independent argumentation, and blind arbitration. We evaluate five instantiations on 200 stratified questions from SycophancyEval. All PAD variants (AnCifer, DeWin, FeynStein, BurGal, Trident) significantly outperform the single-model baseline (18.5%) and instructed-opposition baseline (29.0%), with DeWin achieving 48.5% accuracy (z=6.36, p<0.001 versus both). The variants are not significantly different from each other at n=200. The BurGal variant achieves 53.0% but functions as an architectural validity check; its consensus/heterodox axis structurally favors the heterodox model on every benchmark question. A pre-training floor affects an estimated 40% of questions; fine-tuned disposition models are the identified next step.
11 pages, 3 figures. Code and data available at this http URL
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2606.07532 [cs.CL]
(or arXiv:2606.07532v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.07532 Focus to learn more arXiv-issued DOI via DataCite
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