New research with @AdtRaghunathan, Nicholas Carlini and Anthropic! We built ImpossibleBench to measure reward hacking in LLM coding agents 🤖, by making benchmark tasks impossible and seeing whether models game tests or follow specs. (1/9)

Excited to share our work with my amazing collaborators, @Goodeat258, @SimulatedAnneal, @zicokolter, and Kaiming. In a word, we show an “identity learning” approach for generative modeling, by relating the instantaneous/average velocity in an identity. The resulting model,

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

In diffusion models, samplers are primarily ODE-centric, overlooking slower stochastic methods. However, we show that stochastic sampler can outperform previous samplers on Stable Diffusion, if we use stochasticity correctly! check out Restart Sampling: https://t.co/DT7YoVRyvm