arXiv:2602.11675v4 Announce Type: replace Abstract: Large language models can answer causal questions correctly for the wrong reasons. Current RL methods reward \emph{what} a model concludes but ignore \emph{why}, reinforcing correlational shortcuts -- a failure we call \emph{Reward Entrenchment}. We introduce \emph{Epistemic Regret Minimization} (\erm), a framework that critiques the causal \emph{structure} of a model's reasoning trace rather than its answer. Applying established causal principles, \erm flags unexamined confounders, correlation--intervention conflation, and unchecked back-doo
The development of Epistemic Regret Minimization emerges as a direct response to the recognized limitations of current reinforcement learning methods in evaluating causal reasoning in large language models.
This framework offers a critical advancement in developing more robust and trustworthy AI systems by addressing fundamental flaws in how models learn causal relationships, moving beyond mere outcome prediction.
The focus shifts from rewarding 'what' an AI concludes to critiquing 'why,' potentially leading to AI models that reason more reliably and avoid spurious correlations.
- · AI safety researchers
- · Developers of critical AI applications
- · Users relying on AI for causal inference
- · Developers relying solely on outcome-based RL
- · AI systems prone to correlational shortcuts
Improved reliability and explainability of large language models in complex decision-making tasks.
Reduced incidence of AI-driven errors stemming from faulty causal reasoning in critical applications like medicine or finance.
Accelerated development of genuinely autonomous AI agents capable of nuanced, ethical decision-making based on sound causal understanding.
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Read at arXiv cs.AI