🔥The 9th Round of Easy Loan, Earn $40 Reward is in progress❗️ ⏰ Promotion Period: January 15th - Feburary 15th, 2025 👉 Register now and check more details at gate.io/campaigns/358

Internship alert! We have an immediate part-time research intern opening at Microsoft’s Office of Applied Research to improve LLM reasoning. Please reach out if you or your students are interested!

🤖 Tired of slow tree searches on LLMs? 🚀 Check out our latest research on efficient tree search! 🔹 We introduce an upgraded transformer architecture that enables token-level self-reward modeling (TRM). 🔹 On top of that, we developed the Streaming Looking Ahead (SLA)

📢 𝐖𝐢𝐥𝐝𝐅𝐞𝐞𝐝𝐛𝐚𝐜𝐤 A large-scale preference dataset built from 𝐫𝐞𝐚𝐥 𝐮𝐬𝐞𝐫 interactions with ChatGPT ✅ 𝟐𝟎𝐤+ preference pairs 🗣️ Built from 𝟏𝐌 chats 🔍 Annotated with 𝐝𝐢𝐚𝐥𝐨𝐠𝐮𝐞 𝐬𝐭𝐚𝐭𝐞, 𝐝𝐨𝐦𝐚𝐢𝐧, 𝐢𝐧𝐭𝐞𝐧𝐭, and more huggingface.co/datasets/micro…

#ICLR2025 Oral LLMs often struggle with reliable and consistent decisions under uncertainty 😵‍💫 — largely because they can't reliably estimate the probability of each choice. We propose BIRD 🐦, a framework that significantly enhances LLM decision making under uncertainty. BIRD

🚀 How well can LLMs know you and personalize your response? Turns out, not so much! Introducing the PersonaMem Benchmark -- 👩🏻‍💻Evaluate LLM's ability to understand evolving persona from 180+ multi-session user-chatbot conversation history 🎯Latest models (GPT-4.1, GPT-4.5,

Will be at #NAACL this week. Let's talk if you are interested in RL, agents, and LLM post training in general!

Want to 𝐜𝐮𝐭 𝐑𝐅𝐓 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐭𝐢𝐦𝐞 𝐛𝐲 𝐮𝐩 𝐭𝐨 𝟐× and boost performance? 🚀 Meet 𝑨𝒅𝒂𝑹𝑭𝑻 — a lightweight, plug-and-play curriculum learning method you can drop into any mainstream RFT algorithms (PPO, GRPO, REINFORCE). Less compute. Better results. 🧵 1/n

This is so true. LLM researchers seem to like to "specialize" in either pretraining or post training. Doing intense research on both sides does unlock something.

LLMs naturally memorize some verbatim of pre-training data. We study whether post-training can be an effective way to mitigate unintentional reproduction of pre-training data. 🛠️ No changes to pre-training or decoding 🔥 Training models to latently distinguish between memorized

Join us if you want to work on the next-gen collaborative, socially- intelligent agents!

🔥🔥Let's start cooking 😎😎

Missing nuance in the collective realization today: The non-trivial negative result is not that "RL just amplifies skills that are already there with low probability". Duh, that's obvious and not an issue actually. What got questioned today is that "dumb pretraining teaches the

Huge congrats Ashish Sharma!!👏👏

We know Attention and its linear-time variants, such as linear attention and State Space Models. But what lies in between? Introducing Log-Linear Attention with: - Log-linear time training - Log-time inference (in both time and memory) - Hardware-efficient Triton kernels