Your curated collection of saved posts and media
🚀 Introducing DeepInfer, our in-house inference framework, now fully open source! Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable. 🔗 https://t.co/JGAik1kgk0 1/4
Hi all, I have a new result I'm excited to share. It turns out that *all existing implementations* of softmax and cross-entropy (that's JAX, PyTorch, textbooks, etc) have a subtle bug --- two bugs, actually. A simple rewrite fixes both, and eliminates "slingshots" when grokking: https://t.co/z6yQeJWad1
how do you fine-tune a decision model on your own data? in these two notebooks, you'll learn: • how to prepare the training data • how to fine-tune our d1-3B decision model • using @UnslothAI and @huggingface TRL all within 30 minutes on a free-tier GPU. unsloth: https://t.co/VxwfzlwHXP TRL: https://t.co/yqzvFwVoZ5 ps: how adorable are these halloween kitties? 👻
Introducing Reinforcement Learning from Collective Rewards (RLCR). RLCR is a recipe that trains language models as agent organisations, which share a message board and workspace. We use it to train Avalon-M1-Mini (120B) and study its behaviour. We find a number of interesting results, including: 🛠️ Learnt organisational hierarchies and divisions of labour 🕵️ Increased collective verification, through hires of verifiers and testers 👥 The rise of collective thinking, where agents think more about other agents and how to help them We believe RLCR can provide a laboratory for studying collective intelligence and multi-agent safety. Blog post and technical report 👇
how do you post-train an agent? we laid it out as a 5-step curriculum, from SFT to RL through a real harness, with livestreams, code and docs for each step https://t.co/qTFRnkkYA7 https://t.co/62R5NqntKp
GPT-6.1 Terra beats Astra in our vision benchmarks wtf OpenAI? had to double-check the results - text - object detection - counting also ~47% faster and ~92% cheaper ↓ leaderboards and examples https://t.co/4AvboaD16g
GPT-6.1 Terra beats Astra in our vision benchmarks wtf OpenAI? had to double-check the results - text - object detection - counting also ~47% faster and ~92% cheaper ↓ leaderboards and examples https://t.co/4AvboaD16g
GPT-6.1 Terra: delivering fast and capable computer use at a lower price. GPT-6.1 Terra understands what’s on screen, takes action across apps, writes naturally, and is a more capable model for your whole workday with the efficiency to take on more of it. https://t.co/8JTN1yonmV
Super interesting result: H-Net learns to dynamically tokenize variable names in coding data as separate tokens, and it scales much better on code compared to BPE
I've always believed that intelligence should come from simple, general building blocks that learn their own abstractions directly from raw data; in other words, the bitter lesson. I think we're finally seeing a real step towards AI that can learn from all data. Language modelin
Muse Spark open weights (even in NVFP4) is an amazing computer use agent I shipped an e2e tutorial on building an env for a browser agent (with OpenEnv, Helium and @huggingface Sandboxes) enjoy 🤗 here's a small visualized run ⤵️ https://t.co/LrgZ3GJmO7
Today we’re releasing ClaimBench, our new benchmark targeted towards decision models. It tests whether a model can tell if a financial document supports, contradicts, or doesn’t address a claim. We evaluated 15 models on 1,960 claims paired with 10-K excerpts, with Decider V1.2 leading the pack of decision models.
