Resolve training-inference discrepancy in reverse reasoning

Resolve the training-inference discrepancy, or exposure bias, that remains unresolved in the Reverse Enhanced Thinking (REvThink) approach for training student models with bidirectional reasoning paths.

Background

The paper reviews Reverse Enhanced Thinking (REvThink), which trains a student model using bidirectional reasoning paths. It notes that the method is bounded by the student model’s forward reasoning capacity and that its data-filtering procedure may exclude difficult cases relevant to out-of-distribution generalization.

In addition to these limitations, the paper explicitly identifies the mismatch between training and inference behavior—described as exposure bias—as an unresolved issue. The open problem is therefore to address this discrepancy so that reasoning behavior learned from the training trajectories remains reliable during inference.

References

Finally, the training-inference discrepancy (exposure bias) remains an unresolved issue.

— Construting Reverse Thinking: Developing Large Language Models' Reverse Thingking Ability  (2609.24760 - Liu et al., 21 Sep 2026) in Section 1, subsection “Long Chain-of-Thought (Long CoT) reasoning,” paragraph on Feasible Reflection and subsequent discussion of REvThink