Large Language Models (LLMs) have become increasingly reliant on Reinforcement Learning from Human Feedback (RLHF) for fine-tuning across various applications, including code generation, mathematical reasoning, and dialogue assistance. However, a significant challenge has emerged in the form of reduced output diversity when using RLHF. Research has identified a critical trade-off between alignment quality and output… →

Modern AI systems rely heavily on post-training techniques like supervised fine-tuning (SFT) and reinforcement learning (RL) to adapt foundation models for specific tasks. However, a critical question remains unresolved: do these methods help models memorize training data or generalize to new scenarios? This distinction is vital for building robust AI systems capable of handling real-world… →

Post-training techniques, such as instruction tuning and reinforcement learning from human feedback, have become essential for refining language models. But, open-source approaches often fall behind proprietary models due to a lack of transparency in training data, methodologies, and optimization techniques. Despite the availability of foundational models, the absence of robust, publicly available post-training recipes creates… →

The rapid advancement of Large Language Models (LLMs) has significantly improved their ability to generate long-form responses. However, evaluating these responses efficiently and fairly remains a critical challenge. Traditionally, human evaluation has been the gold standard, but it is costly, time-consuming, and prone to bias. To mitigate these limitations, the LLM-as-a-Judge paradigm has emerged, leveraging… →

Agentic AI stands at the intersection of autonomy, intelligence, and adaptability, offering solutions that can sense, reason, and act in real or virtual environments with minimal human oversight. At its core, an “agentic” system perceives environmental cues, processes them in light of existing knowledge, arrives at decisions through reasoning, and ultimately acts on those decisions—all… →

Knowledge Tracing (KT) plays a crucial role in Intelligent Tutoring Systems (ITS) by modeling students’ knowledge states and predicting their future performance. Traditional KT models, such as Bayesian Knowledge Tracing (BKT) and early deep learning-based approaches like Deep Knowledge Tracing (DKT), have demonstrated effectiveness in learning student interactions. However, recent advancements in deep sequential KT… →

Knowledge graphs have been used tremendously in the field of enterprise lately, with their applications realized in multiple data forms from legal persons to registered capital and shareholder’s details. Although graphs have high utility, they have been criticized for intricate text-based queries and manual exploration, which obstruct the extraction of pertinent information. With the massive… →