MR-Align: Meta-Reasoning Informed Factuality Alignment for Large Reasoning Models
This addresses factuality issues in large reasoning models for applications like question-answering, though it is incremental as it builds on existing alignment methods by focusing on the reasoning process.
The paper tackles the problem of large reasoning models (LRMs) failing to incorporate correct facts into final responses, reducing factual fidelity, by proposing MR-ALIGN, a meta-reasoning informed alignment framework that improves accuracy and truthfulness across factual QA datasets and a long-form benchmark.
Large reasoning models (LRMs) show strong capabilities in complex reasoning, yet their marginal gains on evidence-dependent factual questions are limited. We find this limitation is partially attributable to a reasoning-answer hit gap, where the model identifies the correct facts during reasoning but fails to incorporate them into the final response, thereby reducing factual fidelity. To address this issue, we propose MR-ALIGN, a Meta-Reasoning informed alignment framework that enhances factuality without relying on external verifiers. MR-ALIGN quantifies state transition probabilities along the model's thinking process and constructs a transition-aware implicit reward that reinforces beneficial reasoning patterns while suppressing defective ones at the atomic thinking segments. This re-weighting reshapes token-level signals into probability-aware segment scores, encouraging coherent reasoning trajectories that are more conducive to factual correctness. Empirical evaluations across four factual QA datasets and one long-form factuality benchmark show that MR-ALIGN consistently improves accuracy and truthfulness while reducing misleading reasoning. These results highlight that aligning the reasoning process itself, rather than merely the outputs, is pivotal for advancing factuality in LRMs.