ReDraft is a reference‑driven revision method for continual post‑training of large multimodal language models. It uses the model’s own incorrect outputs as references, revises them, verifies the revisions, and fine‑tunes on the accepted ones, thereby combining explicit supervision with policy proximity. On tasks such as Counting, Clock Reading, and Jigsaw, ReDraft outperforms standard supervised fine‑tuning and on‑policy methods, achieving higher target‑task gains while dramatically reducing forgetting.
By Zhihao Zhang, Mingqi Wu, Qiaole Dong, Enyu Zhou, Shuo Li, Boyang Liu, Jiazheng Zhang, Honglin Guo, Xin Guo, Shaofan Liu, Junzhe Wang, Dingwei Zhu, Zhiheng Xi, Minlong Peng, Yuan Hua, Qi Zhang, Tao Gui, Xuanjing Huang
arXiv:2601. 13020v2 Announce Type: replace-cross Abstract: Continual instruction tuning (CIT) requires multimodal large language models (MLLMs) to adapt to a stream of tasks without forgetting prior capabilities.
By Zhiyan Hou, Haiyun Guo, Haokai Ma, Yandu Sun, Yonghui Yang, Jinqiao Wang
The paper introduces Skill Schema Transport (SST), a post‑training framework that mitigates catastrophic forgetting in continual domain adaptation by translating new domain knowledge into a model’s existing skill space. SST abstracts each example type into a reusable skill schema, preserving essential operations while removing domain‑specific surface forms, thereby turning knowledge updates into procedural reuse rather than trajectory memorization. Experiments on two 3B instruction‑tuned backbones show that SST consistently reduces forgetting and improves generalization, achieving up to a 5.75‑point performance gain and a dramatic drop in maximum forgetting from 42.41 to 7.10 points on the Qwen‑3B MATH stream.
By Zijie Liu, Zhen Tan, Charles Fleming, Tianlong Chen
arXiv:2610.09835v1 Announce Type: new
Abstract: Continual pre-training and fine-tuning in Large Language Models (LLMs) inevitably induce catastrophic forgetting, typically mitigated by replay using o...
By Jonghyun Han, Younghoon Song, Jongyoul Park
The paper introduces Subspace Tuning (SST), a fine‑tuning technique that restricts weight updates to the bottom singular subspace of model parameters, thereby protecting the principal directions that are most prone to catastrophic forgetting. SST is optimizer‑agnostic, works with existing fine‑tuning recipes such as SFT and self‑distillation, and includes dense gradient estimation and a refresh mechanism to enhance learning. Experiments show that SST can preserve up to 99 % of the weight subspace, significantly reduce KL drift, and nearly eliminate forgetting in large‑scale language models while maintaining strong target‑task performance.
By Arnav Chavan, Kusha Sareen, Rishabh Tiwari, Abhranil Chandra, Johan Obando-Ceron, Kurt Keutzer, Scott Niekum, Inderjit S Dhillon, Devvrit Khatri
Monarch-MoE introduces structured experts for multimodal continual instruction tuning by replacing dense LoRA factors with block‑diagonal ones linked by a fixed permutation, enabling blockwise gradient isolation. This design reduces adapter parameters by up to 75% and 50% on two benchmarks while keeping first‑task forgetting below 0.52 and 0.10 percentage points, respectively. The method is optimized for memory‑limited hardware, allowing fine‑tuning and inference on a single 24 GB RTX 4090 with lower memory usage and competitive runtime.
By Xi Zhang, Boyu Diao, Zesen Chen, Ruiqi Liu, Hangda Liu, Yongjun Xu