Tag
This paper studies context design for self-distillation in language models, finding that step-aligned critique feedback significantly outperforms binary reward or reference solution conditioning, because it targets only erroneous tokens while preserving correct behavior.
This paper evaluates the biological plausibility and representational alignment of feedback alignment algorithms in convolutional networks, comparing them to standard backpropagation on CIFAR-10. The authors find that modified feedback alignment methods converge on internal representations similar to those produced by backpropagation, suggesting functional success through mimicking representational geometry.