# Semantic Supervision

SemanTok is a flexible-length video tokenizer that feeds frozen DINOv2 features into its encoder and adds lightweight heads that reconstruct those features from each retained token prefix alone, letting a 201M autoregressive model match or beat a VideoFlexTok model 3.4 times its size and improving semantic alignment at every noise level, including pure noise.

SemanTok is a flexible-length video tokenizer that feeds frozen DINOv2 features into its encoder and adds lightweight heads that reconstruct those features from each retained token prefix alone, letting a 201M autoregressive model match or beat a VideoFlexTok model 3.4 times its size and improving semantic alignment at every noise level, including pure noise.

The work proposes BiasReducer, a lightweight framework that edits only the linear reward head without retraining the reward model: an SAE-style encoder with semantic supervision learns internal representations for predefined attributes such as length, confidence, and sycophancy; the framework then determines the direction and amount by which to adjust the reward head for each attribute and stores these as an edit bank; for a new dataset it ranks attributes by their influence on reward scores and applies the matching edits. Across five reward models, BiasReducer-M improves RM-Bench-Hard, Arena-StyleConflict, and JudgeBiasBench by 8.3, 18.0, and 6.

The work proposes BiasReducer, a lightweight framework that edits only the linear reward head without retraining the reward model: an SAE-style encoder with semantic supervision learns internal representations for predefined attributes such as length, confidence, and sycophancy; the framework then determines the direction and amount by which to adjust the reward head for each attribute and stores these as an edit bank; for a new dataset it ranks attributes by their influence on reward scores and applies the matching edits. Across five reward models, BiasReducer-M improves RM-Bench-Hard, Arena-StyleConflict, and JudgeBiasBench by 8.3, 18.0, and 6.