A factored causal representation learning method improves robustness of reward models in RLHF by isolating causal factors from biases like length and sycophancy using adversarial gradient reversal.
Causal re- ward adjustment: Mitigating reward hacking in exter- nal reasoning via backdoor correction.arXiv preprint arXiv:2508.04216, 2025a
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Factored Causal Representation Learning for Robust Reward Modeling in RLHF
A factored causal representation learning method improves robustness of reward models in RLHF by isolating causal factors from biases like length and sycophancy using adversarial gradient reversal.