Contrastive Consolidation of Top-Down Modulations Achieves Sparsely Supervised Continual Learning

arXiv — cs.LG•Wednesday, November 5, 2025 at 5:00:00 AM

Contrastive Consolidation of Top-Down Modulations Achieves Sparsely Supervised Continual Learning

A novel method termed task-modulated contrastive learning (TMCL) has been proposed to improve continual learning in machine learning systems by drawing inspiration from biological brain processes. TMCL is designed to learn effectively from both unlabeled and sparsely labeled data, reflecting how biological brains acquire knowledge. This approach aims to address the prevalent challenge of catastrophic forgetting, where models lose previously acquired knowledge when learning new tasks. By incorporating top-down modulations in a contrastive learning framework, TMCL seeks to maintain robust performance across multiple tasks over time. The method's design supports the prevention of catastrophic forgetting while enhancing the system's ability to consolidate knowledge continuously. Overall, TMCL represents a promising advancement in sparsely supervised continual learning by aligning machine learning strategies with biological learning mechanisms.

— via World Pulse Now AI Editorial System

Read Original

Was this article worth reading? Share it

Recommended Readings

arXiv — cs.LG13 hours ago

A Comparative Analysis of LLM Adaptation: SFT, LoRA, and ICL in Data-Scarce Scenarios

NeutralArtificial Intelligence

This article explores various methods for adapting Large Language Models (LLMs) in data-scarce scenarios, focusing on techniques like SFT, LoRA, and ICL. It highlights the challenges of full fine-tuning, including its high computational cost and the risk of catastrophic forgetting, while discussing alternative approaches that can help maintain general reasoning abilities.

Read full article

via arXiv — cs.LG

arXiv — cs.CL13 hours ago

Mixture of Routers

PositiveArtificial Intelligence

Recent advancements in machine learning highlight the benefits of combining Low-Rank Adaptation (LoRA) with Mixture-of-Experts (MoE) to improve the performance of large language models. While LoRA has been recognized for its efficiency in parameter usage, its impact alone has been limited. This new approach could lead to significant enhancements in fine-tuning, making it an exciting development in the field.

Read full article

via arXiv — cs.CL

arXiv — cs.LG13 hours ago

In Situ Training of Implicit Neural Compressors for Scientific Simulations via Sketch-Based Regularization

PositiveArtificial Intelligence

A new training protocol for implicit neural representations is introduced, utilizing limited memory buffers and sketched data to avoid catastrophic forgetting. This innovative approach is backed by theoretical insights from the Johnson-Lindenstrauss lemma, making it relevant for continual learning in scientific simulations.

Read full article

via arXiv — cs.LG

arXiv — cs.LG13 hours ago

Path-Coordinated Continual Learning with Neural Tangent Kernel-Justified Plasticity: A Theoretical Framework with Near State-of-the-Art Performance

PositiveArtificial Intelligence

A new framework for continual learning addresses the issue of catastrophic forgetting in neural networks. By integrating the Neural Tangent Kernel theory with statistical validation and path quality evaluation, this approach shows promising results and enhances the learning process.

Read full article

via arXiv — cs.LG

arXiv — cs.LG2 days ago

RL Fine-Tuning Heals OOD Forgetting in SFT

PositiveArtificial Intelligence

Recent research highlights the effectiveness of combining Supervised Fine-Tuning (SFT) with Reinforcement Learning (RL) to enhance the reasoning capabilities of Large Language Models (LLMs). This two-stage fine-tuning approach not only improves performance but also challenges the oversimplified notion that SFT merely memorizes while RL generalizes. Understanding this synergy is crucial as it could lead to more robust AI systems that better handle out-of-distribution scenarios, ultimately benefiting various applications in technology and research.

Read full article

via arXiv — cs.LG

arXiv — cs.LG2 days ago

Knowledge-guided Continual Learning for Behavioral Analytics Systems

NeutralArtificial Intelligence

A recent study discusses the challenges faced by behavioral analytics systems as user behavior on online platforms evolves. It highlights the issue of data drift, which can degrade model performance over time, and the risks of catastrophic forgetting when fine-tuning models with new data. This research is significant as it addresses the need for improved methods to maintain the effectiveness of these systems in capturing user interactions, ensuring they remain relevant and accurate.

Read full article

via arXiv — cs.LG

arXiv — cs.CL2 days ago

JudgeLRM: Large Reasoning Models as a Judge

NeutralArtificial Intelligence

A recent study highlights the growing use of Large Language Models (LLMs) as evaluators, presenting them as a scalable alternative to human annotation. However, the research points out that current supervised fine-tuning methods often struggle in areas that require deep reasoning. This is particularly important because judgment involves more than just scoring; it includes verifying evidence and justifying decisions. Understanding these limitations is crucial as it informs future developments in AI evaluation methods.

Read full article

via arXiv — cs.CL

arXiv — cs.CL3 days ago

VCORE: Variance-Controlled Optimization-based Reweighting for Chain-of-Thought Supervision

PositiveArtificial Intelligence

The recent introduction of VCORE, a new method for variance-controlled optimization-based reweighting, marks a significant advancement in the field of supervised fine-tuning for large language models. This approach addresses the limitations of traditional methods by recognizing that not all tokens in a reasoning trajectory contribute equally to the learning process. By improving how models are trained on complex reasoning tasks, VCORE promises to enhance the overall reasoning capabilities of these models, making them more effective in real-world applications.

Read full article

via arXiv — cs.CL