Semantic Reward Collapse and the Preservation of Epistemic Integrity in Adaptive AI Systems

Problem
This preprint addresses a critical gap in the understanding of reinforcement learning from human feedback (RLHF) and preference optimization in large language models. The authors identify recurring problematic behaviors—such as performative certainty and calibration drift—that arise from the scalarization of diverse evaluative signals into a generalized optimization framework. These issues suggest that existing models may not adequately preserve epistemic integrity, leading to a collapse of distinct semantic categories into a singular reward signal, which can obscure the model’s ability to express uncertainty and factual correctness.

Method
The core technical contribution is the introduction of Semantic Reward Collapse (SRC), which describes how distinct forms of evaluative dissatisfaction (e.g., factual inaccuracies, uncertainty disclosure, and social preferences) can become conflated within a shared reward topology. The authors propose Constitutional Reward Stratification (CRS) as a novel framework designed to maintain differentiated epistemic attribution in adaptive learning systems. CRS aims to protect uncertainty disclosure and escalation behaviors from being globally penalized, thereby promoting a more nuanced approach to reward structuring. The paper does not disclose specific architectural details, loss functions, or training compute requirements, focusing instead on the conceptual framework and its implications for future research.

Results
The paper does not present empirical results or quantitative evaluations against established baselines or benchmarks, as it primarily proposes a theoretical framework rather than a fully implemented system. The authors emphasize that CRS is a governance-oriented research direction requiring further empirical validation, indicating that the effectiveness of this approach remains to be tested in practical scenarios.

Limitations
The authors acknowledge that their proposed framework, CRS, is not yet validated and requires empirical investigation to assess its effectiveness in real-world applications. They do not address potential challenges in implementing CRS, such as the complexity of integrating domain-aware reward structures into existing RLHF systems or the risk of introducing new forms of bias in reward stratification. Additionally, the lack of empirical results limits the ability to evaluate the practical implications of SRC and CRS.

Why it matters
This work has significant implications for the design and governance of adaptive AI systems, particularly in the context of RLHF. By highlighting the risks associated with Semantic Reward Collapse, the authors advocate for a shift in how evaluative signals are structured and interpreted within these systems. The proposed CRS framework could lead to more robust models that better handle uncertainty and factual correctness, ultimately enhancing the reliability and safety of AI systems in critical applications. This research direction encourages further exploration into the preservation of epistemic integrity, which is essential for the responsible deployment of AI technologies.

Authors: William Parris
Source: arXiv:2605.12406
URL: https://arxiv.org/abs/2605.12406v1