Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs... Beyond the Base Model? New paper questions the common assumption that RLVR helps LLMs acquire novel reasoning abilities.show more

Excited to share CrystalReasoner, a reasoning model for crystal... structure generation with LLMs and property-conditioned generation through RL: Website: Paper: Code:show more

Fin-R1 is out on Hugging Face A Large Language... Model for Financial Reasoning through Reinforcement Learningshow more

ICML 2026: Latent Reasoning in TRMs is Secretly a... Policy Improvement Operator Why does recursive reasoning, especially latent reasoning, actually work? The theory is still young, and even mechanistic explanations are limited. We close part of this gap by showing that latent reasoning is secretly doing policy improvement. Each recursion pushes the model steadily toward the target. Based on this view, we propose an algorithm that boosts learning and inference efficiency by up to 18x.show more

New paper! LLMs Corrupt Your Documents When You Delegate... LLMs are enabling a new way of working: delegated work, where users supervise an LLM as it edits documents on their behalf. Delegation requires trust: does the LLM complete tasks without introducing errors? We simulate delegation across 52 professional domains and find that LLMs Corrupt Your Documents When You Delegate. 🧵1/Nshow more

In our blog post, you can interactively explore the... reasoning effect of token cues and RL on base models! 📎 paper: ✍ blog: 💻 code:show more

Can “chicken” make a base model reason? 🐔 Yes!... Why? With the right first tokens, base models can match RL in reasoning. We trace this to learned training data associations and use a simple data edit to make “chicken” elicit reasoning too. blog:show more

We are launching a new stealth model - "Andromeda... Alpha" This is a smaller reasoning model that has been trained to be really good at image understanding.show more

Ever wondered how training dynamics differ between LLMs 🖋️... and Vision 👁️ models? We explore this and close the gap between VMs and LLMs in our #NeurIPS2024 paper "TrAct: Making First-layer Pre-Activations Trainable". Paper📜 Video🎥show more

Current Vision-Language-Action (VLA) paradigms in autonomous driving primarily rely... on Imitation Learning (IL), which introduces inherent challenges such as distribution shift and causal confusion. Online Reinforcement Learning offers a promising pathway to address these issues through trial-and-error learning. However, applying online reinforcement learning to VLA models in autonomous driving is hindered by inefficient exploration in continuous action spaces. MindDrive, a VLA framework comprising a large language model (LLM) with two distinct sets of LoRA parameters. The one LLM serves as a Decision Expert for scenario reasoning and driving decision-making, while the other acts as an Action Expert that dynamically maps linguistic decisions into feasible trajectories. Paper Title: MindDrive: A Vision-Language-Action Model for Autonomous Driving via Project: Link:show more

Transformers & LLMs cheatsheets for Stanford's CME-295! Covering tokenization,... self-attention, prompting, fine-tuning, LLM-as-a-judge, RAG, AI Agents, and reasoning models. 100% free and open-source.show more

Learn how LLMs work under the hood! This is... the best interactive website to learn how LLMs work. It combines clear, step-by-step explanations with dynamic 3D visualizations for an intuitive learning experience.show more

Everyone's obsessing over which LLM is smarter. Wrong question.... The real bottleneck in AI right now isn't reasoning. It's what the model is reasoning on. Garbage data in, confident garbage out. - Footage via Opus 5.5show more

Try 👁 Agentic Vision with Gemini 3 Flash in... Google AI Studio or Vertex AI. This new capability enables the model to effectively use code and reasoning to improve performance for common vision tasks. See Agentic Vision in action:show more

New research project: Lluminate - an evolutionary algorithm that... helps LLMs break free from generating predictable, similar outputs. Combining evolutionary principles with creative thinking strategies can illuminate the space of possibilities.show more

Today, we enter a new era of intelligence with... Gemini 3. ✨ Our most intelligent model helps you bring any idea to life — offering state-of-the-art reasoning with unprecedented depth and nuance.show more

Google has just launched an AI feature capable of... planning, reasoning and searching the web. All this is done in a single prompt using Deep Research. This is literally the first reasoning model that has access to the internet. Game-changer. How to use it below 🧵show more

🚨How do LLMs acquire human values?🤔 We often point... to preference optimization. However, in our new work, we trace how and when model values shift during post-training and uncover surprising dynamics. We ask: How do data, algorithms, and their interaction shape model values?🧵show more

RL is back! But is it always the best... choice? In a new paper, we investigate under what circumstances neuroevolution outperforms RL in transfer learning tasks. See more details in the thread below 🧵 While NE performs best in simpler domains, it will be interesting to see if the lessons learned here can also be applied to more complex systems/tasks (LLMs?).show more

1/ RL for improving task success has made LLMs... remarkably capable. But what happens when the model fails? Turns out, training for graceful failure can be surprisingly tricky! Very excited to share our new paper: Training Language Models to Fail Gracefully 🧵show more