Learning When to Trust in Contextual Bandits
arXiv:2603.13356v1 Announce Type: new Abstract: Standard approaches to Robust Reinforcement Learning assume that feedback sources are either globally trustworthy or globally adversarial. In this paper, we challenge this assumption and we identify a more subtle failure mode. We term this mode as Contextual Sycophancy, where evaluators are truthful in benign contexts but strategically biased in critical ones. We prove that standard robust methods fail in this setting, suffering from Contextual Objective Decoupling. To address this, we propose CESA-LinUCB, which learns a high-dimensional Trust Boundary for each evaluator. We prove that CESA-LinUCB achieves sublinear regret $\tilde{O}(\sqrt{T})$ against contextual adversaries, recovering the ground truth even when no evaluator is globally reliable.
arXiv:2603.13356v1 Announce Type: new Abstract: Standard approaches to Robust Reinforcement Learning assume that feedback sources are either globally trustworthy or globally adversarial. In this paper, we challenge this assumption and we identify a more subtle failure mode. We term this mode as Contextual Sycophancy, where evaluators are truthful in benign contexts but strategically biased in critical ones. We prove that standard robust methods fail in this setting, suffering from Contextual Objective Decoupling. To address this, we propose CESA-LinUCB, which learns a high-dimensional Trust Boundary for each evaluator. We prove that CESA-LinUCB achieves sublinear regret $\tilde{O}(\sqrt{T})$ against contextual adversaries, recovering the ground truth even when no evaluator is globally reliable.
Executive Summary
This article proposes a novel approach to addressing a subtle failure mode in robust reinforcement learning known as Contextual Sycophancy. In this setting, evaluators are truthful in benign contexts but strategically biased in critical ones. The authors demonstrate that standard robust methods fail in this setting, suffering from Contextual Objective Decoupling. To address this, they introduce CESA-LinUCB, a framework that learns a high-dimensional Trust Boundary for each evaluator. The authors provide theoretical guarantees for CESA-LinUCB, demonstrating its ability to achieve sublinear regret against contextual adversaries. This work has significant implications for robust reinforcement learning and highlights the importance of considering contextual trustworthiness in evaluations.
Key Points
- ▸ Contextual Sycophancy: a subtle failure mode in robust reinforcement learning where evaluators are truthful in benign contexts but strategically biased in critical ones
- ▸ Standard robust methods fail in this setting, suffering from Contextual Objective Decoupling
- ▸ CESA-LinUCB: a framework that learns a high-dimensional Trust Boundary for each evaluator
- ▸ Theoretical guarantees for CESA-LinUCB: sublinear regret against contextual adversaries
Merits
Strength
The authors provide a clear and concise definition of Contextual Sycophancy, highlighting a previously overlooked failure mode in robust reinforcement learning.
Strength
The proposed framework, CESA-LinUCB, offers a novel approach to addressing Contextual Sycophancy, with theoretical guarantees for achieving sublinear regret.
Demerits
Limitation
The authors assume that the Trust Boundary for each evaluator can be learned, which may not be feasible in all scenarios.
Limitation
The proposed framework may require significant computational resources to learn the high-dimensional Trust Boundaries.
Expert Commentary
The article presents a novel and significant contribution to the field of robust reinforcement learning. The authors' definition of Contextual Sycophancy is clear and concise, and the proposed framework, CESA-LinUCB, offers a promising approach to addressing this failure mode. However, the authors' assumptions about the learnability of the Trust Boundary for each evaluator may be limiting, and the computational resources required to learn these boundaries may be significant. Nevertheless, this work has significant implications for the field and highlights the importance of considering contextual trustworthiness in evaluations.
Recommendations
- ✓ Further research is needed to explore the applicability of CESA-LinUCB to other areas of reinforcement learning.
- ✓ The proposed framework may be extended to consider multiple evaluators with different levels of trustworthiness.