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Mitigating racial bias from LLMs is a lot easier than removing it from humans! Can’t believe this happened at the best AI conference NeurIPS Conference We have ethical reviews for authors, but missed it for invited speakers? 😡

#NeurIPS2024 Super fun talking to tons of people yesterday. Like seeing people got genuinely surprised and laughed. Non stop 3 hrs talking. Finally the poster session is over and I can take a break :). Looking forward to seeing new research inspired by #Trace. Great job

I'm hiring researchers for my physically embodied AI & robotics team at MSR! 🤖👇 jobs.careers.microsoft.com/us/en/job/1778… Physically embodied agents, both in the humanoid robot form and beyond, are the new computational platform of tomorrow. As with personal computers many decades ago, these

MSR is hiring robotics researchers!! Good time to join! 😎

Announcing AutoGen 0.4, fully reimagined library for building advanced agentic AI systems, developed to improve code quality and robustness. Its asynchronous, event-driven architecture is designed to support dynamic, scalable workflows. Learn more: msft.it/6012ohgli

The RLC accepted workshops list is out (link in next tweet)! Programmatic RL Causal RL RL and videogames Inductive biases and RL and returning from last year: RL beyond rewards, finding the frame, and RL in practice!

Started my new job at #Google Research recently. Super excited about what can be done here. 😎

Our ICML & RLC workshops welcome contributions using programmatic representations as policies, reward functions, skill libraries, task generators, environment models, etc., to improve interpretability, generalization, efficiency, & safety in agent learning & RL! Please retweet 🙏

Check out this new optimization framework (github.com/datarobot/syftr) by #DataRobot that can automatically search for "Pareto-optimal" solutions for agentic workflows. It's built on our LLM generative optimization framework #Trace. Excited to see more applications of #Trace! 😎

Decision-making with LLM can be studied with RL! Can an agent solve a task with text feedback (OS terminal, compiler, a person) efficiently? How can we understand the difficulty? We propose a new notion of learning complexity to study learning with language feedback only. 🧵👇

Super excited about this work done by our former intern Wanqiao Xu . We show Learning from Language Feedback (LLF) with LLM can be formally studied with provable no-regret learning algorithms. This result builds a foundation toward new theories for LLM learning and optimization.