aiqbench analysis
DynaCF addresses a critical flaw in reward model training where models exploit superficial shortcut cues instead of learning genuine response quality. By dynamically downweighting samples with high shortcut sensitivity during optimization, this framework forces models to rely on task-relevant signals, thereby improving robustness in preference modeling. This approach offers a practical method for enhancing the reliability of RLHF pipelines without relying on static heuristics.
DynaCF: Mitigating Shortcut Learning in Reward Models via Dynamic Counterfactual Sensitivity
arXiv:2606.09043v1 Announce Type: new Abstract: Reward models trained from pairwise preferences often exploit superficial shortcut cues rather than learning true response quality. We propose DynaCF, a dynamic reweighting framework for mitigating shortcut learning in reward model training. Unlike static shortcut heuristics, DynaCF measures shortcut sensitivity online during optimization by applying semantics-preserving counterfactual perturbations and tracking the resulting margin shifts and preference flips under the current model. Samples with higher shortcut sensitivity are dynamically downweighted in the Bradley-Terry objective, encouraging the model to rely less on superficial patterns and more on task-relevant preference signals. Extensive experiments show that DynaCF consistently improves robustness in preference modeling.
Abstract:Reward models trained from pairwise preferences often exploit superficial shortcut cues rather than learning true response quality. We propose DynaCF, a dynamic reweighting framework for mitigating shortcut learning in reward model training. Unlike static shortcut heuristics, DynaCF measures shortcut sensitivity online during optimization by applying semantics-preserving counterfactual perturbations and tracking the resulting margin shifts and preference flips under the current model. Samples with higher shortcut sensitivity are dynamically downweighted in the Bradley-Terry objective, encouraging the model to rely less on superficial patterns and more on task-relevant preference signals. Extensive experiments show that DynaCF consistently improves robustness in preference modeling.
Machine Learning (cs.LG); Computation and Language (cs.CL)
arXiv:2606.09043 [cs.LG]
(or arXiv:2606.09043v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.09043 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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