因果一致性正则化：在大型语言模型中训练可验证的敏感推理
Causal Consistency Regularization: Training Verifiably Sensitive Reasoning in Large Language Models
AI总结：
CSR通过强制因果一致性提升大语言模型的推理忠实性，实现更可靠的结构化领域推理。
摘要：
大型语言模型可以在依赖有缺陷的推理轨迹的同时产生正确的答案，部分原因是常见的训练目标奖励最终答案的正确性而不是忠实的中间推理。这在高风险场景中削弱了可信度。我们提出了反事实敏感性正则化（CSR），一种训练范式，通过强制推理步骤与结果之间的因果一致性来提高推理的忠实性。CSR会自动对推理轨迹应用操作级干预，例如将
英文摘要：
Large language models can produce correct answers while relying on flawed reasoning traces, partly because common training objectives reward final-answer correctness rather than faithful intermediate reasoning. This undermines trustworthiness in high-stakes settings. We propose Counterfactual Sensitivity Regularization (CSR), a training paradigm that improves reasoning faithfulness by enforcing causal consistency between reasoning steps and outcomes. CSR automatically applies operator-level interventions to reasoning traces, such as swapping "+" with "-", to generate minimally perturbed counterfactual rationales, and penalizes the model when these logically invalid traces still lead to the original answer. Our implementation is efficient, adding about 9 percent training overhead via a warm-start curriculum and token-subset optimization. We evaluate faithfulness using Counterfactual Outcome Sensitivity (COS), which measures how appropriately answers change under logical perturbations. Across arithmetic (GSM8K), logical deduction (ProofWriter), multi-hop question answering (HotpotQA), and code generation (MBPP), CSR yields improved accuracy versus faithfulness trade-offs, establishing a new Pareto frontier. CSR improves faithfulness over standard fine-tuning and process supervision by up to 70 percentage points, and transfers across model families with 94.2 to 96.7 percent success in structured domains. CSR also complements inference-time methods such as self-consistency. Overall, CSR offers a practical route to more reliable reasoning in structured domains, including mathematics, formal logic, and code, where operators are well-defined and verifiable, covering an estimated 40 to 60 percent of high-stakes reasoning deployments.
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