DIR applies an information bottleneck to reward model training to mitigate complex inductive biases such as length, sycophancy, and format, with claimed improvements in RLHF generalization.
My final verdict is tie: [[A=B]]
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Eliminating Inductive Bias in Reward Models with Information-Theoretic Guidance
DIR applies an information bottleneck to reward model training to mitigate complex inductive biases such as length, sycophancy, and format, with claimed improvements in RLHF generalization.