NLP Papers
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Emergent Hierarchical Reasoning in LLMs Through Reinforcement Learning
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
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…
The Era of Hierarchical Reasoning Models?
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?
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
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?
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
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)
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
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
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
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…
SWE-RL by Meta — Reinforcement Learning for Software Engineering LLMs
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?
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?
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?
Discover s1: a simple yet powerful approach to test-time scaling for LLMs, rivaling o1-preivew with just 1k samples!…
DeepSeek-R1 Paper Explained – A New RL LLMs Era in AI?
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?
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?
Discover how System 2 thinking through Monte Carlo Tree Search enables rStar-Math to rival OpenAI’s o1 in math, using Small Language Models!…