With the release of DeepSeek R1, there is a buzz in the AI community. The open-source model offers some best-in-class performance across many metrics, even at par with state-of-the-art proprietary models in many cases. Such huge success invites attention and curiosity to learn more about it. In this article, we will look into implementing a …
Artificial intelligence has grown significantly with the integration of vision and language, allowing systems to interpret and generate information across multiple data modalities. This capability enhances applications such as natural language processing, computer vision, and human-computer interaction by seamlessly allowing AI models to process textual, visual, and video inputs. However, challenges remain in ensuring that…
Large language models (LLMs) have shown remarkable abilities in language tasks and reasoning, but their capacity for autonomous planning—especially in complex, multi-step scenarios—remains limited. Traditional approaches often rely on external verification tools or linear prompting methods, which struggle with error correction, state tracking, and computational efficiency. This gap becomes evident in benchmarks like Blocksworld, where…
Novel view synthesis has witnessed significant advancements recently, with Neural Radiance Fields (NeRF) pioneering 3D representation techniques through neural rendering. While NeRF introduced innovative methods for reconstructing scenes by accumulating RGB values along sampling rays using multilayer perceptrons (MLPs), it encountered substantial computational challenges. The extensive ray point sampling and large neural network volumes created…
The advancements in large language models (LLMs) have significantly enhanced natural language processing (NLP), enabling capabilities like contextual understanding, code generation, and reasoning. However, a key limitation persists: the restricted context window size. Most LLMs can only process a fixed amount of text, typically up to 128K tokens, which limits their ability to handle tasks…
Open Source LLM development is going through great change through fully reproducing and open-sourcing DeepSeek-R1, including training data, scripts, etc. Hosted on Hugging Face’s platform, this ambitious project is designed to replicate and enhance the R1 pipeline. It emphasizes collaboration, transparency, and accessibility, enabling researchers and developers worldwide to build on DeepSeek-R1’s foundational work. What…
Mixture-of-Experts (MoE) models utilize a router to allocate tokens to specific expert modules, activating only a subset of parameters, often leading to superior efficiency and performance compared to dense models. In these models, a large feed-forward network is divided into smaller expert networks, with the router—typically an MLP classifier—determining which expert processes each input. However,…
Reinforcement learning (RL) focuses on enabling agents to learn optimal behaviors through reward-based training mechanisms. These methods have empowered systems to tackle increasingly complex tasks, from mastering games to addressing real-world problems. However, as the complexity of these tasks increases, so does the potential for agents to exploit reward systems in unintended ways, creating new…