arXiv Machine Learning
Jul 7
RL Forgets! Towards Continual Policy Optimization
arXiv:2607. 04364v1 Announce Type: new Abstract: Continual post-training is becoming a central paradigm for adapting vision-language models to evolving tasks.
Hidden Forgetting in Continual Multimodal Learning: When Accuracy Survives but Grounding Fails
arXiv:2607. 02020v1 Announce Type: new Abstract: Multimodal large language models must continually adapt to evolving tasks and domains, yet standard continual learning metrics mainly measure whether old answers remain correct, leaving the stability of multimodal grounding largely unexamined.
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arXiv Machine Learning
Jul 31
Regularizing modality contribution drift in multimodal continual learning
arXiv:2607. 27260v1 Announce Type: new Abstract: Multimodal continual learning (MMCL) aims to learn emerging knowledge from multimodal data while preserving knowledge.
arXiv Machine Learning
Jul 10
When Does Continual Learning Require Learning
arXiv:2607. 07847v1 Announce Type: new Abstract: As large language models (LLMs) become increasingly capable, the next question is how can we enable models to continually learn?
arXiv AI
Aug 7
CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning
arXiv:2605. 20247v2 Announce Type: replace-cross Abstract: Catastrophic forgetting remains a major obstacle to continual learning in large language models (LLMs) and vision--language models (VLMs).
arXiv Machine Learning
Jul 20
Rethinking Transfer in Continual Learning: A Replay-Based Realisation
arXiv:2607. 15587v1 Announce Type: new Abstract: Continual learning studies how deployed language models can continually acquire new tasks without expensive retraining from scratch.
arXiv AI
Aug 19
When to Review: Spaced Repetition for Continual Pre-Training of Language Models
The paper introduces Spaced Repetition Training (SRT), a continual learning framework that schedules sample rehearsal using the SM-2 algorithm. SRT tracks per-example review states and maps perplexity to recall quality, allowing the training loop to decide which examples to replay and when. Experiments on Wikipedia and code corpora show that SRT improves the stability‑plasticity trade‑off, recovers 5–37 percentage points of lost old‑knowledge accuracy, and preserves benchmark performance better than naive continual pre‑training or uniform replay.
Hugging Face Trending Papers
Aug 18
When to Review: Spaced Repetition for Continual Pre-Training of Language Models
The paper introduces Spaced Repetition Training (SRT), a continual learning framework that adapts review scheduling for language models by using the SM-2 algorithm to decide which past examples to replay. SRT tracks per-example review states and maps perplexity to a recall-quality signal, allowing the model to retain old knowledge while consolidating new information without changing the underlying model or training objective. Experiments on Wikipedia and code corpora show that SRT improves the stability-plasticity trade‑off, recovers 5–37 percentage points of lost accuracy, and maintains benchmark performance better than naive continual pre‑training or uniform replay; similar benefits are observed in vision and tabular data when an appropriate recall signal is used.
Hugging Face Trending Papers
Aug 26
A Visual Dependence-Aware Framework for Multimodal Unsupervised Continual Post-Training
In this paper, we explore a novel task of Multimodal Unsupervised Continual Post-Training (MU-CPT), enabling deployed MLLMs to continually evolve from streaming unlabeled data. Existing unsupervised p...
arXiv Computer Vision
Aug 27
A Visual Dependence-Aware Framework for Multimodal Unsupervised Continual Post-Training
The paper introduces a new task called Multimodal Unsupervised Continual Post-Training (MU‑CPT), which allows multimodal large language models (MLLMs) to continuously learn from streaming unlabeled data. It identifies token‑level visual dependence (VD) as essential for MU‑CPT, using its structural distortion to detect cross‑modal forgetting and its heterogeneity to guide new‑task learning. The proposed Visual Dependence‑Aware (VDA) framework includes Visually Constrained Optimal Transport (VC‑OT) to mitigate forgetting and Visually Modulated Adaptation (VMA) to enhance new‑task plasticity, achieving a balance between stability and adaptability in MU‑CPT.
arXiv Machine Learning
Jun 5
Catastrophic Forgetting as Accessibility Collapse: A Three-Level Framework for Knowledge Persistence in Continual Learning
arXiv:2606. 06032v1 Announce Type: new Abstract: Catastrophic forgetting is commonly interpreted as the irreversible erasure of previously acquired knowledge during sequential learning.
arXiv AI
Jun 19
Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models
arXiv:2510. 21978v2 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has delivered impressive gains in mathematical and multimodal reasoning and has become a standard post-training paradigm for contemporary language and vision-language models.
arXiv Machine Learning
Sep 25
Beyond Forgetting: Diagnosing and Harnessing Shared Reasoning in Continual RLVR
The paper introduces Continual Reasoning Gym, a continual‑RLVR environment that sequences text and visual reasoning tasks. It finds that while sequential RLVR shows modest forgetting, its final performance lags behind multitask RLVR (MTRL) because forgetting explains only part of the gap. To bridge this, the authors propose Continual Prompt Replay (CPR), which replays previous‑task prompts and regenerates responses with the current policy, achieving on average MTRL‑level performance.