Jiahe Jin

🔥 Excited to share our work "Efficient Agent Training for Computer Use" Q: Do computer use agents need massive data or complex RL to excel? A: No, with just 312 high-quality trajectories, Qwen2.5-VL can outperform Claude 3.7, setting a new SOTA for Windows computer use. 1/6

🔥 Happy to share our paper on test-time scaling (TTS)! 🚀 We take the position that generative AI has entered Act II, that is cognition engineering driven by TTS. 🛠️ We provide many valuable resources to help community utilize TTS to develop the cognitive ability of models.

Introduction to Operator & Agents openai.com/index/introduc…

🤔Dreaming of AI agents that can handle complex work? Frustrated by the endless hunt for agent training data? 🚀Introducing PC Agent & PC Tracker, our human cognition transfer framework enabling AI to perform complex computer tasks: 📹 PC Tracker: the first lightweight…

🤔 Struggling to train capable AI agents due to lack of quality data? 🚀 Meet PC Tracker & PC Agent - our groundbreaking system that learns from real human computer operation process to handle complex digital work! Watch how PC Agent automatically creates slides about Attention…

🚀 Still relying on human-crafted rules to improve pretraining data? Time to try Programming Every Example(ProX)! Our latest efforts use LMs to refine data with unprecedented accuracy, and brings up to 20x faster training in general and math domain! 👇 Curious about the details?

The Alpaca moment of Large Multimodal Models! Can we build native LMMs just like Llama for simple multimodal generation? Introducing Anole: the first open-source, autoregressive native LMM for multimodal generation. Building on Chameleon by @AIatMeta: github.com/GAIR-NLP/anole

🚀How can we effectively evaluate and prevent superintelligent LLMs from deceiving others? We introduce 🤝BeHonest, a pioneering benchmark specifically designed to assess the honesty in LLMs comprehensively. Paper 📄: [arxiv.org/abs/2406.13261] Code 👨🏻‍💻: [github.com/GAIR-NLP/BeHon…]…