Hugging Face Trending Papers
Behaviorally Effective LoRA Writes Are Sparse and Structured
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
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.
Hugging Face Trending Papers
Jul 15
Dysco: Dynamic Subspace Boosting to Mitigate LoRA Interference in Federated Learning
Federated fine-tuning of large pre-trained models increasingly relies on Low-Rank Adaptation (LoRA) to reduce communication and computation, but heterogeneous clients can make adapter aggregation unstable. We identify the data-parameter interference as a geometric source of this instability.
arXiv Machine Learning
Jul 17
Dysco: Dynamic Subspace Boosting to Mitigate LoRA Interference in Federated Learning
arXiv:2607. 14367v1 Announce Type: new Abstract: Federated fine-tuning of large pre-trained models increasingly relies on Low-Rank Adaptation (LoRA) to reduce communication and computation, but heterogeneous clients can make adapter aggregation unstable.
arXiv Machine Learning
Sep 29
New LoRA Skills Should Read but Never Write
The paper introduces READ, a method for composing low‑rank adapters (LoRA) in large language models. By rewriting each adapter into a balanced canonical form and enforcing a one‑directional coupling, READ allows new skills to read but never write into the output subspaces of existing skills, eliminating interference. Experiments on four benchmark suites and two model families show that READ consistently outperforms existing baselines, improving SuperGLUE scores by over twenty points and domain suite scores by more than seven points.
arXiv Machine Learning
Oct 23
Delta N-gram: Forward-Only Continual Writing into a Language Model's Native N-gram Memory
Delta N-gram introduces a forward‑only write path into a language model’s native hashed N‑gram table, preserving the pretrained table while adding evidence through a sparse patch Δ. The method uses a 4.1 M‑parameter writer that emits gated targets per event and applies a block‑averaged delta rule to keep each row a convex combination of its old value and the new targets. Experiments on a 0.6 B prototype and Qwen3.8‑Flash‑Next show that the delta writer maintains more document‑specific memory and reduces drift compared to online gradient writes, achieving stable performance over thousands of writes. whyItMatters":"The approach allows continual updates to a language model’s memory without altering its pretrained parameters, enabling efficient, stable, and document‑specific knowledge retention during inference."
arXiv AI
Oct 2
Does Scaling Reinforcement Learning Really Require More Training?
arXiv:2610.01133v1 Announce Type: cross Abstract: Scaling reasoning typically spends more compute on reinforcement learning (RL) or on inference. We show that a completed RL training history can yiel...