I've been getting a lot of DMs about Jev and how it relates to our RLCR (RL with Calibration Rewards) paper. Whatever the connection, anyone who's talked research with me in the last year knows I can go on and on about calibration as one of the most overlooked parts of post-training, so I'm really happy to see it finally getting attention. If we want models that are actually useful in the real world, in healthcare, law, or scientific discovery, they need to know what they don't know. That's why I've spent so long thinking about better paradigms for uncertainty-aware decision making. There's still so much to do: training more calibrated models, building systems that interact well with users in uncertain settings (life🙂), and figuring out how to evaluate all of it in a way that reflects real workflows instead of completion based rewards. DMs open if you want to chat! (congrats to @CompleteSkeptic on the launch, and thanks to @rasbt for the great writeup!) 📄 arxiv.org/pdf/2507.16806

It's never made sense to me that RL collapses all reward signals to a single scalar. Today, we fix that! Introducing Vector Policy Optimization: we train models to inherently optimize for the varied nature of a reward vector, creating diverse sets of answers ideal for test time search. Website and code coming soon!