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Hugging Face Trending Papers

The Gradient Does Not See Rank: Rank-Indifference in Matrix-CODI on ProsQA

The paper investigates whether the rank of matrix-valued latent representations in continuous chain‑of‑thought models influences task accuracy. Experiments on ProsQA and GSM8K‑Aug show that truncating the latent matrix to low rank has negligible effect (within 0.6 pp), and this flatness persists across various readout designs and even in a vanilla GPT‑2 baseline. The results suggest that rank is not a useful structural signal for these models’ reasoning paths.

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arXiv Machine Learning
Sep 4

The Gradient Does Not See Rank: Rank-Indifference in Matrix-CODI on ProsQA

The paper investigates whether the rank of latent matrices in matrix‑chain‑of‑thought (Matrix‑CODI) models influences performance on reasoning tasks. Across multiple training regimes on ProsQA and GSM8K‑Aug, rank‑k projection ablations show flat accuracy curves, indicating that truncating the latent matrix to low rank does not hurt performance. Experiments with various readout architectures—bilinear, bilinear‑plus‑GELU, SVD‑augmented, and quadratic—confirm that rank‑indifference persists even for nonlinear readouts, and a linear probe on the latent matrix underperforms a raw pretrained hidden state.

By Samuel Larson (Pebble ML)
llmsfine-tuningbenchmarks
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arXiv Computation and Language
Sep 2

Behaviorally Effective LoRA Writes Are Sparse and Structured

The paper investigates which parts of a low‑rank adaptation (LoRA) update actually drive behavior in language models. It shows that behaviorally effective LoRA writes are sparse, structured, and highly concentrated, with only a few module‑wise components carrying most of the signal. Using a learned‑basis continuation recipe, the authors demonstrate that these sparse, structured writes can be recovered with minimal error and that they remain robust across different training continuations and evaluation tasks.

By Haruto Sato, Yuki Tanaka, Ren Nakamura, Aoi Kobayashi, Mei Ito
fine-tuning
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Hugging Face Trending Papers
Sep 2

LoRA-TSD: Tangent-Space Spectral Descent for LoRA via Muon-Style Updates

Low-rank adaptation (LoRA) is the standard way to fine-tune large models, yet when its two factors are trained independently, the update ignores the geometry of the low-rank weight change it induces. We introduce LoRA-TSD, an optimizer that treats every LoRA step as a tangent vector of the fixed-rank matrix manifold and takes the spectral-norm steepest-descent step of Muon inside that tangent space, mapping the result back to the factors through a retraction native to the LoRA parametrization.

llmsfine-tuningbenchmarks
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arXiv Machine Learning
4d ago

Rank-Efficient LoRA via Joint Tangent-Space Optimization under Isotropic Curvature

The paper introduces ISO-LoRA, an optimizer that improves rank utilization in Low‑Rank Adaptation (LoRA) by coupling factor updates through spectral descent on the induced tangent perturbation in weight space. Experiments on GPT‑2 adaptation show that standard optimizers like AdamW concentrate updates in a few singular directions, whereas ISO-LoRA distributes energy more evenly, leading to higher effective rank and better downstream performance across 0.1B‑7B models. The authors provide theoretical guarantees under a stylized spiked‑gradient model and demonstrate that ISO-LoRA consistently outperforms factor‑wise optimizers, especially at moderate‑to‑large LoRA ranks.

By Zihan Zhu, Zhehang Du, Xuyang Chen, Tim Tsz-Kit Lau, Jiayuan Wu, X. Y. Han, Qi Long, Weijie Su
llmsfine-tuning
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