The Paradox of Outcome Optimization: A Causal Information-Theoretic Bound on Reasoning Shortcuts in LLMs
arXiv:2606.00674v1 Announce Type: new Abstract: Large Language Models (LLMs) aligned via outcome-based Reinforcement Learning (RL) frequently exhibit a critical failure mode: they achieve high performance on in-distribution benchmarks while demonstrating brittle reasoning capabilities on out-of-distribution (OOD) tasks. We term this phenomenon Reward-Induced Manifold Collapse. We establish a theoretical framework bridging Structural Causal Models (SCM) and the Information Bottleneck (IB) principle to explain this paradox. We define reasoning as a high-complexity causal process and shortcut lea
The proliferation of LLMs and their deployment in various applications highlights the urgency of understanding foundational limitations in AI alignment and reasoning. This research surfaces at a time when 'good enough' performance is being heavily scrutinized for robustness and generalization.
A strategic reader should care because this research elucidates a core limitation in current AI development paradigms, specifically how reward functions can inadvertently lead to brittle intelligence rather than robust reasoning. It directly impacts the reliability and trustworthiness of LLM-powered systems.
This theoretical framework changes our understanding of alignment strategies, suggesting that outcome-based optimization might inherently bake in reasoning shortcuts, necessitating new approaches for building truly generalized and causally robust AI. It shifts focus from purely performance metrics to the underlying causal reasoning capabilities of LLMs.
- · AI safety researchers
- · Developers of causal AI models
- · Enterprises reliant on robust AI
- · Developers focused solely on outcome optimization
- · Current Reinforcement Learning (RL) based alignment techniques without causal co
Increased focus on causal inference and information theory in AI research to overcome 'Reward-Induced Manifold Collapse'.
Development of new AI alignment techniques that prioritize causal reasoning over mere outcome optimization, potentially leading to more robust and trustworthy AI systems.
Long-term re-evaluation of AI deployment strategies, particularly in high-stakes domains, due to a clearer understanding of LLM reasoning limitations.
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Read at arXiv cs.LG