The Paradox of Outcome Optimization: A Causal Information-Theoretic Bound on Reasoning Shortcuts in LLMs

2026-05-30 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

Large Language Models (LLMs) aligned through outcome-based Reinforcement Learning (RL) frequently exhibit "Reward-Induced Manifold Collapse," a phenomenon where they achieve high performance on in-distribution benchmarks but demonstrate brittle reasoning on out-of-distribution tasks. This paper establishes a theoretical framework, integrating Structural Causal Models (SCM) and the Information Bottleneck (IB) principle, to explain this paradox. It defines reasoning as a high-complexity causal process and shortcut learning as exploiting low-complexity spurious correlations. The authors show that Stochastic Gradient Descent (SGD) implicitly biases models toward shortcut solutions when training distributions enable "Markovian Screening" of the true causal mechanism. A new generalization bound, based on Semantic Coverage Measure ($η$) rather than sample size, is derived, illustrating why data scaling on homogeneous distributions may not correct reasoning flaws. Furthermore, Process Reward Models (PRMs) are presented as Topological Filters, enforcing step-wise mutual information constraints that render low-complexity shortcut manifolds inadmissible, providing mathematical grounding for process supervision.

Key takeaway

For Machine Learning Engineers developing and aligning LLMs, recognize that optimizing solely for outcome rewards can induce "Reward-Induced Manifold Collapse," leading to brittle out-of-distribution performance. You should prioritize incorporating process supervision, such as Process Reward Models (PRMs), into your alignment strategies. This approach enforces step-wise mutual information constraints, effectively filtering out low-complexity reasoning shortcuts and improving the model's true causal reasoning capabilities, even with extensive data scaling.

Key insights

Outcome-based RL causes LLMs to learn shortcuts, leading to brittle OOD reasoning, a problem process supervision can address.

Principles

• Reward-Induced Manifold Collapse explains LLM OOD brittleness.
• Shortcut learning exploits low-complexity spurious correlations.
• Process Reward Models act as Topological Filters.

Method

A theoretical framework combines Structural Causal Models (SCM) and Information Bottleneck (IB) to explain shortcut learning, deriving a generalization bound based on Semantic Coverage Measure ($η$). This framework mathematically grounds process supervision.

Topics

• Large Language Models
• Reinforcement Learning
• Process Supervision
• Causal Models
• Out-of-Distribution Generalization
• Shortcut Learning

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.