Language models have made significant strides in tackling reasoning tasks, with even small-scale supervised fine-tuning (SFT) approaches such as LIMO and s1 demonstrating remarkable improvements in mathematical problem-solving capabilities. However, fundamental questions remain about these advancements: Do these models genuinely generalise beyond their training data, or are they merely overfitting to test sets? The research The post LLMs Can Now Solve Challenging Math Problems with Minimal Data: Researchers from UC Berkeley and Ai2 Unveil a Fine-Tuning Recipe That Unlocks Mathematical Reasoning Across Difficulty Levels appeared first on MarkTechPost.

The domain of LLMs has rapidly evolved to include tools that empower these models to integrate external knowledge into their reasoning processes. A significant advancement in this direction is Retrieval-Augmented Generation (RAG), which allows models to query databases and search engines for up-to-date or niche information not embedded during training. RAG enhances performance in knowledge-intensive The post LLMs Can Now Learn to Try Again: Researchers from Menlo Introduce ReZero, a Reinforcement Learning Framework That Rewards Query Retrying to Improve Search-Based Reasoning in RAG Systems appeared first on MarkTechPost.

Despite rapid advances in vision-language modeling, much of the progress in this field has been shaped by models trained on proprietary datasets, often relying on distillation from closed-source systems. This reliance creates barriers to scientific transparency and reproducibility, particularly for tasks involving fine-grained image and video understanding. Benchmark performance may reflect the training data and The post Meta AI Released the Perception Language Model (PLM): An Open and Reproducible Vision-Language Model to Tackle Challenging Visual Recognition Tasks appeared first on MarkTechPost.

Web scraping and data extraction are crucial for transforming unstructured web content into actionable insights. Firecrawl Playground streamlines this process with a user-friendly interface, enabling developers and data practitioners to explore and preview API responses through various extraction methods easily. In this tutorial, we walk through the four primary features of Firecrawl Playground: Single URL The post An In-Depth Guide to Firecrawl Playground: Exploring Scrape, Crawl, Map, and Extract Features for Smarter Web Data Extraction appeared first on MarkTechPost.

The integration of Large Language Models (LLMs) with external tools, applications, and data sources is increasingly vital. Two significant methods for achieving seamless interaction between models and external systems are Model Context Protocol (MCP) and Function Calling. Although both approaches aim to expand the practical capabilities of AI models, they differ fundamentally in their architectural The post Model Context Protocol (MCP) vs Function Calling: A Deep Dive into AI Integration Architectures appeared first on MarkTechPost.