# AI Papers Academy

> Simplifying AI Papers


## Posts

- [DeepSeek's mHC Explained: Manifold-Constrained Hyper-Connections](https://aipapersacademy.com/deepseek-mhc/): Manifold-Constrained Hyper-Connections (mHC) explained: How DeepSeek rewires residual connections in LLMs for next-gen AI
- [Emergent Hierarchical Reasoning in LLMs Through Reinforcement Learning](https://aipapersacademy.com/emergent-hierarchical-reasoning-in-llms/): Discover how reinforcement learning enables hierarchical reasoning in LLMs and how HICRA improves on top of GRPO.
- [Less Is More: Tiny Recursive Model (TRM) Paper Explained](https://aipapersacademy.com/tiny-recursive-model/): In this post, we break down the TRM paper, a simpler version of the HRM,  that beats HRM and top reasoning LLMs with a tiny 7M params model.
- [DINOv3 Paper Explained: The Computer Vision Foundation Model](https://aipapersacademy.com/dinov3/): In this post we break down Meta AI's DINOv3 research paper, which introduces a state-of-the-art Computer Vision foundation models family
- [The Era of Hierarchical Reasoning Models?](https://aipapersacademy.com/hierarchical-reasoning-model/): In this post we break down the Hierarchical Reasoning Model (HRM), a new model that rivals top LLMs on reasoning benchmarks with only 27M params!
- [Microsoft’s Reinforcement Pre-Training (RPT) - A New Direction in LLM Training?](https://aipapersacademy.com/reinforcement-pre-training/): In this post we break down Microsoft's Reinforcement Pre-Training, which scales up reinforcement learninng with next-token reasoning  
- [Darwin Gödel Machine: Self-Improving AI Agents](https://aipapersacademy.com/darwin-godel-machine/): In this post we explain the Darwin Gödel Machine, a novel method for self-improving AI agents by Sakana AI
- [Continuous Thought Machines (CTMs) - The Era of AI Beyond Transformers?](https://aipapersacademy.com/continuous-thought-machines/): Dive into Continuous Thought Machines, a novel architecture that strive to push AI closer to how the human brain works
- [Perception Language Models (PLMs) by Meta – A Fully Open SOTA VLM](https://aipapersacademy.com/perception-language-models/): Dive into Perception Language Models by Meta, a family of fully open SOTA vision-language models with detailed visual understanding
- [GRPO Reinforcement Learning Explained (DeepSeekMath Paper)](https://aipapersacademy.com/deepseekmath-grpo/): DeepSeekMath is the fundamental GRPO paper, the reinforcement learning method used in DeepSeek-R1. Dive in  to understand how it works
- [DAPO: Enhancing GRPO For LLM Reinforcement Learning](https://aipapersacademy.com/dapo/): Explore DAPO, an innovative open-source Reinforcement Learning paradigm for LLMs that rivals DeepSeek-R1 GRPO method.
- [Cheating LLMs & How (Not) To Stop Them | OpenAI Paper Explained](https://aipapersacademy.com/cheating-llms/): Discover how OpenAI's research reveals AI models cheating the system through reward hacking — and what happens when trying to stop them
- [START by Alibaba: Teaching LLMs To Debug Themselves](https://aipapersacademy.com/self-taught-reasoner-with-tools/): In this post we break down a recent Alibaba’s paper: START: Self-taught Reasoner with Tools. This paper shows how Large Language Models (LLMs) can teach themselves to debug their own thinking using Python. Introduction Top reasoning models, such as DeepSeek-R1, achieve remarkable results with long chain-of-thought (CoT) reasoning. These models are presented with complex problems as input, and through an extended reasoning process, also known as long CoT, the models can repeatedly reevaluate their solutions before providing a final answer. However, when it comes to very complex problems, even these models may struggle to arrive at the correct solution. One […]
- [SWE-RL by Meta — Reinforcement Learning for Software Engineering LLMs](https://aipapersacademy.com/swe-rl/): Dive into SWE-RL by Meta, a DeepSeek-R1 style recipe for training LLMs for software engineering with reinforcement learning.
- [Large Language Diffusion Models: The Era Of Diffusion LLMs?](https://aipapersacademy.com/large-language-diffusion-models/): Discover Large Language Diffusion Models (LLaDA), a novel diffusion based approach to language modeling that challenges traditional LLMs.
- [CoCoMix by Meta AI - The Future of LLMs Pretraining?](https://aipapersacademy.com/cocomix/): Discover CoCoMix by Meta AI - a new approach for LLM pretraining using Continuous Concept Mixing, enriching word tokens with latent concepts!
- [s1: Simple Test-Time Scaling - Can 1k Samples Rival o1-Preview?](https://aipapersacademy.com/s1/): Discover s1: a simple yet powerful approach to test-time scaling for LLMs, rivaling o1-preivew with just 1k samples!
- [DeepSeek Janus Pro Paper Explained - Multimodal AI Revolution?](https://aipapersacademy.com/janus-pro/): Dive into DeepSeek Janus Pro, another magnificent open-source release, this time a multimodal AI model that rivals top multimodal models!
- [DeepSeek-R1 Paper Explained - A New RL LLMs Era in AI?](https://aipapersacademy.com/deepseek-r1/): Dive into the groundbreaking DeepSeek-R1 research paper, introduces open-source reasoning models that rivals the performance OpenAI's o1!
- [Titans by Google: The Era of AI After Transformers?](https://aipapersacademy.com/titans/): Dive into Titans, a new AI architecture by Google, showing promising results comparing to Transformers! Paving the way for a new era in AI?
- [rStar-Math by Microsoft: Can SLMs Beat OpenAI o1 in Math?](https://aipapersacademy.com/rstar-math/): Discover how System 2 thinking through Monte Carlo Tree Search enables rStar-Math to rival OpenAI's o1 in math, using Small Language Models!
- [Large Concept Models (LCMs) by Meta: The Era of AI After LLMs?](https://aipapersacademy.com/large-concept-models/): Explore Meta's Large Concept Models (LCMs) - an AI model that processes concepts instead of tokens. Can it become the next LLM architecture?
- [Byte Latent Transformer (BLT) by Meta AI: A Tokenizer-free LLM Revolution](https://aipapersacademy.com/byte-latent-transformer/): Explore Byte Latent Transformer (BLT) by Meta AI: A tokenizer-free LLM that scales better than tokenization-based LLMs
- [Coconut by Meta AI - Better LLM Reasoning With Chain of CONTINUOUS Thought?](https://aipapersacademy.com/chain-of-continuous-thought/): Discover how Meta AI's Chain of Continuous Thought (Coconut) empowers large language models (LLMs) to reason in their own language.
- [Hymba by NVIDIA: A Hybrid Mamba-Transformer Language Model](https://aipapersacademy.com/hymba/): Discover NVIDIA's Hymba model that combines Transformers and State Space Models for state-of-the-art performance in small language models
- [LLaMA-Mesh by Nvidia: LLM for 3D Mesh Generation](https://aipapersacademy.com/llama-mesh/): Dive into Nvidia's LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models, a LLM which was adapted to understand 3D objects.
- [Tokenformer: Rethinking Transformer Scaling with Tokenized Model Parameters](https://aipapersacademy.com/tokenformer/): Dive into Tokenformer, a novel architecture that improves Transformers to support incremental model growth without training from scratch
- [Generative Reward Models: Merging the Power of RLHF and RLAIF for Smarter AI](https://aipapersacademy.com/generative-reward-models/): In this post we dive into a Stanford research presenting Generative Reward Models, a hybrid Human and AI RL to improve LLMs
- [Sapiens by Meta AI: Foundation for Human Vision Models](https://aipapersacademy.com/sapiens/): In this post we dive into Sapiens, a new family of computer vision models by Meta AI that show remarkable advancement in human-centric tasks!
- [Mixture of Nested Experts: Adaptive Processing of Visual Tokens](https://aipapersacademy.com/mixture-of-nested-experts/): In this post we dive into Mixture of Nested Experts, a new method presented by Google that can dramatically reduce AI computational cost
- [Introduction to Mixture-of-Experts | Original MoE Paper Explained](https://aipapersacademy.com/mixture-of-experts/): Diving into the original Google paper which introduced the Mixture-of-Experts (MoE) method, which was critical to AI progress
- [Mixture-of-Agents Enhances Large Language Model Capabilities](https://aipapersacademy.com/mixture-of-agents/): In this post we explain the Mixture-of-Agents method, which shows a way to unite open-source LLMs to win GPT-4o on AlpacaEval 2.0
- [Arithmetic Transformers with Abacus Positional Embeddings](https://aipapersacademy.com/abacus-embeddings/): In this post we dive into Abacus Embeddings, which dramatically enhance Transformers arithmetic capabilities with strong logical extrapolation
- [CLLMs: Consistency Large Language Models](https://aipapersacademy.com/consistency-large-language-models/): In this post we dive into Consistency Large Language Models (CLLMs), a new family of models which can dramatically speedup LLMs inference!
- [ReFT: Representation Finetuning for Language Models](https://aipapersacademy.com/reft/): Learn about Representation Finetuning (ReFT) by Stanford University, a method to fine-tune large language models (LLMs) efficiently.
- [Stealing Part of a Production Language Model](https://aipapersacademy.com/stealing-part-of-a-production-language-model/): What if we could discover OpenAI models internal weights? In this post we dive into a paper which presents an attack that steals LLMs data.
- [How Meta AI ‘s Human-Like V-JEPA Works?](https://aipapersacademy.com/v-jepa/): Explore V-JEPA, which stands for Video Joint-Embedding Predicting Architecture. Another step in Meta AI's journey for human-like AI
- [The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits](https://aipapersacademy.com/the-era-of-1-bit-llms/): In this post we dive into the era of 1-bit LLMs paper by Microsoft, which shows a promising direction for low cost large language models
- [Self-Rewarding Language Models by Meta AI](https://aipapersacademy.com/self-rewarding-language-models/): In this post we dive into the Self-Rewarding Language Models paper by Meta AI, which can possibly be a step towards open-source AGI
- [Fast Inference of Mixture-of-Experts Language Models with Offloading](https://aipapersacademy.com/moe-offloading/): Diving into a research paper introducing an innovative method to enhance LLM inference efficiency using memory offloading
- [TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones](https://aipapersacademy.com/tinygpt-v/): In this post we dive into TinyGPT-V, a small but mighty Multimodal LLM which brings Phi-2 success to vision-language tasks
- [LLM in a flash: Efficient Large Language Model Inference with Limited Memory](https://aipapersacademy.com/llm-in-a-flash/): In this post we dive into LLM in a flash paper by Apple, that introduces a method to run LLMs on devices that have limited memory
- [Vision Transformers Explained | The ViT Paper](https://aipapersacademy.com/vision-transformers/): In this post we go back to the important vision transformers paper, to understand how ViT adapted transformers to computer vision
- [Orca 2: Teaching Small Language Models How to Reason](https://aipapersacademy.com/orca-2/): Dive into Orca 2 research paper, the second version of the successful Orca small language model from Microsoft, 
- [From Diffusion Models to LCM-LoRA](https://aipapersacademy.com/from-diffusion-models-to-lcm-lora/): Following LCM-LoRA release, in this post we explore the evolution of diffusion models up to latent consistency models with LoRA
- [CODEFUSION: A Pre-trained Diffusion Model for Code Generation](https://aipapersacademy.com/codefusion/): In this post we dive into Microsoft's CODEFUSION, an approach to use diffusion models for code generation that achieves remarkable results
- [Table-GPT: Empower LLMs To Understand Tables](https://aipapersacademy.com/table-gpt/): In this post we dive into Table-GPT, a novel research by Microsoft, that empowers LLMs to understand tabular data
- [Vision Transformers Need Registers - Fixing a Bug in DINOv2?](https://aipapersacademy.com/vision-transformers-need-registers/): In this post we explain the paper "Vision Transformers Need Registers" by Meta AI, that explains an interesting behavior in DINOv2 features
- [Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack](https://aipapersacademy.com/emu/): In this post we dive into Emu, a text-to-image generation model by Meta AI, which is quality-tuned to generate highly aesthetic images.
- [NExT-GPT: Any-to-Any Multimodal LLM](https://aipapersacademy.com/next-gpt/): In this post we dive into NExT-GPT, a multimodal large language model (MM-LLM), that can both understand and respond with multiple modalities
- [Large Language Models As Optimizers - OPRO by Google DeepMind](https://aipapersacademy.com/large-language-models-as-optimizers/): In this post we dive into the Large Language Models As Optimizers paper by Google DeepMind, which introduces OPRO (Optimization by PROmpting).
- [FACET: Fairness in Computer Vision Evaluation Benchmark](https://aipapersacademy.com/facet/): In this post we cover FACET, a new dataset created by Meta AI in order to evaluate a benchmark for fairness of computer vision models
- [Code Llama Paper Explained](https://aipapersacademy.com/code-llama/): Discover an in-depth review of Code Llama paper, a specialized version of the Llama 2 model designed for coding tasks
- [WizardMath - Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct](https://aipapersacademy.com/wizardmath-best-open-source-math-llm-via-reinforced-evol-instruct/): Diving into WizardMath, a LLM for mathematical reasoning contributed by Microsoft, surpassing models such as WizardLM and LLaMA-2. 
- [Orca Research Paper Explained](https://aipapersacademy.com/orca/): In this post we dive into Orca's paper which shows how to do imitation tuning effectively, outperforms ChatGPT with about 7% of its size!
- [LongNet: Scaling Transformers to 1B Tokens with Dilated Attention](https://aipapersacademy.com/longnet/): In this post we dive into the LongNet research paper which introduced the Dilated Attention mechanism and explain how it works
- [DINOv2 from Meta AI - A Foundational Model in Computer Vision](https://aipapersacademy.com/dinov2-from-meta-ai-finally-a-foundational-model-in-computer-vision/): DINOv2 by Meta AI finally gives us a foundational model for computer vision. We'll explain what it means and why DINOv2 can count as such
- [I-JEPA: The First Human-Like Computer Vision Model](https://aipapersacademy.com/i-jepa-a-human-like-computer-vision-model/): Dive into I-JEPA, Image-based Joint-Embedding Predictive Architecture, the first model based on Yann LeCun's vision for a more human-like AI. 
- [ImageBind: One Embedding Space To Bind Them All](https://aipapersacademy.com/imagebind/): ImageBind is a multimodality model by Meta AI. In this post, we dive into ImageBind research paper to understand what it is and how it works.
- [Consistency Models - Optimizing Diffusion Models Inference](https://aipapersacademy.com/consistency-models/): Consistency models are a new type of generative models which were introduced by Open AI, and in this post we will dive into how they work
- [LIMA from Meta AI - Less Is More for Alignment of LLMs](https://aipapersacademy.com/lima/): In this post we explain LIMA, a LLM by Meta AI which was fine-tuned on only 1000 samples, yet it achieves competitive results with top LLMs
- [Shepherd: A Critic for Language Model Generation](https://aipapersacademy.com/shepherd-a-critic-for-language-model-generation/): Dive into Shepherd, a LLM from Meta AI which is purposed to critique responses from other LLMs, a step in resolving LLMs hallucinations.
- [Universal and Transferable Adversarial LLM Attacks](https://aipapersacademy.com/llm-attacks/): LLMs are aligned for safety to avoid generation of harmful content. In this post we review a paper that is able to successfully attack LLMs.
- [Meta-Transformer: A Unified Framework for Multimodal Learning](https://aipapersacademy.com/meta-transformer/): In this post we dive into Meta-Transformer, a unified framework for multimodal learning, which can process information from 12(!) modalities
- [From Sparse to Soft Mixture of Experts](https://aipapersacademy.com/from-sparse-to-soft-mixture-of-experts/): In this post we review Google DeepMind's paper that introduces Soft Mixture of Experts, a fully-differentiable sparse Transformer.
- [What is YOLO-NAS and How it Was Created](https://aipapersacademy.com/yolo-nas/): YOLO-NAS is an object detection model with the best accuracy-latency tradeoff to date. In this post we explain how it was created.

## Pages

- [Best AI & Machine Learning Theory Books](https://aipapersacademy.com/best-ai-machine-learning-theory-books/): The curated list of books here are intended for deepening your theoretical understanding of AI, helping you understand not just how algorithms work, but why they work. They’re standard references behind university ML courses and research programs, focusing on mathematics, proofs, and conceptual principles. ⚠️ Prerequisites: Books here assume comfort with calculus, linear algebra, probability, and basic machine learning concepts. If you’re new to ML, consider starting with our AI & ML Books for Beginners & Intermediates. Why study theory? Strong mathematical foundations help you: We’ll continue to update this list as new theoretical and research-level books emerge. Disclosure: This […]
- [Best AI & Machine Learning Books for Beginners to Intermediates](https://aipapersacademy.com/best-ai-machine-learning-books-for-beginners/): Best AI & Machine Learning Books for Beginners to Intermediates In this post, we’ve curated book recommendations based on experience and community feedback. The books listed here are trusted entry points into AI and machine learning, recommended by practitioners, educators, and researchers.To help you find your ideal starting point, books are organized into categories. We recommend complete beginners to pick their first book from the Practical Applied Machine Learning section. For more advanced and theoretical books, check out: Best Books on Machine Learning Theory We’ll keep enriching and updating this list as the field evolves. Disclosure: This page contains affiliate […]
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