arXiv AI
Oct 9

Freeze the Decoder, Heal the Encoder: Parameter-Efficient Adaptation for SVD-Based KV-Cache Compression

arXiv:2610. 10552v1 Announce Type: cross Abstract: Comparing parameter-efficient fine-tuning recipes under a single, shared learning rate is a common but flawed practice: when the arms being compared have very different trainable-parameter counts, a shared rate can simultaneously depress the larger arms' means and inflate their variance, manufacturing a large, seemingly multi-seed-significant advantage for the smallest arm that is not a real effect.

By Yufeng Wang
arXiv Machine Learning
Oct 16

Patch where the text is new: a compression view of where byte-level models should spend compute

The paper introduces a novel patching strategy for byte‑level language models, called novelty patching, which starts a new patch when upcoming bytes are unlikely to repeat recent context, inspired by LZ77 compression. Experiments on a 1.9 GB math corpus show that novelty patching improves final‑answer accuracy from 47 % to 72 % at 12 M parameters and raises computed‑result accuracy from 21 % to 57 % at 50 M parameters, while having limited impact on prose and source code. The study also analyzes patch boundary distributions and reports several failed approaches, such as typed uncertainty and context‑aware novelty prediction. whyItMatters":"The findings demonstrate that a simple, data‑driven patching rule can significantly enhance the performance of byte‑level models on tasks involving computed results, offering a practical improvement for large language models that process raw text without explicit labels."

By Nicol\'as Vera Z\'u\~niga
arXiv Machine Learning
Oct 8

Transmission Factors for Lossy Compression of PDE Training Data: Measuring What Reaches a Trained Operator

The paper discusses how operator‑learning benchmarks are stored as full‑precision arrays that have grown to terabyte scale, and curators must decide how coarsely to compress them. It argues that the usual tolerance on reconstruction error of the stored field is measured in the wrong place, because what matters is the accuracy of a model trained on the compressed copy, not the raw field error. The authors propose a new metric: push a compressed field through a model trained at full precision and compare its output to the output on the clean field, which captures the portion of codec error that actually reaches the model’s output. This metric spans more than two orders of magnitude across six PDE families and correctly orders downstream cost where field error does not, reversing many cross‑dataset comparisons.

By Huy Hoang Le