Han Hu
Han Hu — researcher or builder tracked in the Angestrom contributor network.
Papers · 2
Optimizing Visual Generative Models via Distribution-wise Rewards
Conventional reinforcement learning strategies for visual generation typically employ sample-wise reward functions, yet this practice frequently results in reward hacking that degrades image diversity and introduces visual anomalies. To address these limitations, we present a novel framework that finetunes generative models using distribution-wise rewards, ensuring better alignment with real-world data distributions. Unlike rewards that evaluate samples individually, distribution-wise reward accounts for the data distribution of the samples, mitigating the mode collapse problem that occurs whe
Hy-Embodied-VLM-1.0: Efficient Physical-World Agents
Building capable embodied agents requires not only multimodal perception and understanding, but also agentic capabilities for reasoning about actions, adapting to evolving situations, and interacting with the physical world. In this report, we introduce Hy-Embodied-VLM-1.0, an efficient and powerful embodied foundation model specifically designed for embodied agents operating in the physical world. To cultivate such capabilities from the pre-training stage onward, we define an action-centric capability taxonomy comprising three progressive dimensions: Action-Relevant State Understanding, Actio
News · 3
AI is more likely than humans to form biases when hiring - MIT Technology Review
<a href="https://news.google.com/rss/articles/CBMiggFBVV95cUxNWmRKWWxsZGNmWWdsSnF3LUpyOGtXYnRDRS1GZjA4UTFHR3RhYU44V2M2Wkw4eVFfNkp6eTFGVnp5VmlRWjA3Z2RwX2ktdnJFS2ZCRU92dEJ2V2dxZlBzUGotVzNKd0txbzRGQks0TkZlczFNN2VNYnVwRXMwVkhJemdR0gGHAUFVX3lxTE5qQy1oTGZzVWxPSG9hRG1ZZy1HRzBocDlWdGQ2Ml9OXzFQdTI5alFXMGo0Yk9CLU5ZWVhENXJIMFQ0X1dab2xBU2FRMVVGNy02eVZKTzVVMkQya2xpUzRDaXRaMEFvQU92dDB3Mk9NMUJ2MTczVEZ6ZXdmVWxTaG9OMk1YMWFfVQ?oc=5" target="_blank">AI is more likely than humans to form biases when hiring</a> 
Gigatoken: A new open source tokenizer ~100x faster than Tiktoken, -500-1000x faster than Huggingface
  submitted by   <a href="https://www.reddit.com/user/Thrumpwart"> /u/Thrumpwart </a> <br /> <span><a href="https://github.com/marcelroed/gigatoken/#benchmarks">[link]</a></span>   <span><a href="https://www.reddit.com/r/LocalLLaMA/comments/1v2yfqp/gigatoken_a_new_open_source_tokenizer_100x_faster/">[comments]</a></span>
AI is more likely than humans to form biases when hiring
The next time you apply for a job, AI may screen your résumé before any human sees it. But there’s good reason to question whether AI will judge you fairly. Researchers already know that LLMs pick up human biases from their training data. New research suggests that LLMs can also develop their own biases from…