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cs.CL2026
RewardExplainer: Learning Reward Model Explanations from Counterfactual Preference Feedback
Jingyi He, Nier Wu, Shuang Liu +3
Reward models (RMs) are a key component of large language model post-training, providing reward signals for subsequent reinforcement learning. However, conventional discriminative…
cs.CL2026
SAEExplainer: Interpreting SAE Features with Activation-Guided Preference Optimization
Jingyi He, Haiyan Zhao, Ruxue Shi +4
Although Sparse Autoencoders (SAEs) have mitigated the opacity of large language models (LLMs) by decomposing dense representations into sparse features, explaining these features…