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https://t.co/ZHDodYuiIB

Yet ANOTHER rogue swarm has been discovered Did OpenAI disclose this? No. Did OpenAI... even know? Once AGAIN, the only reason we know about this is because the company being attacked went public! How many rogue swarms are still out there? Hundreds? Thousands? https://t.co/Z4o52vt0iY https://t.co/T3uEkAFmXp

full video https://t.co/ydcrN30IhU

ChatGPT Ads for @Shopify is live. We are @OpenAI's first commerce partner. OpenAI pulls straight from Shopify Catalog, so merchants' products are already there. Merchants set their own campaigns, their own budget. Free to install, tracked right from the Shopify admin. Consumers are asking ChatGPT what to buy. Shopify merchants are able to decide exactly how they show up. https://apps.shopify.com/chatgptads

the main thing i was excited about launching this week will be next week instead, but imo worth the wait! https://t.co/j8tcW05Fmk

So OpenAI will now publicly disclose model misalignment even before it fully understands or fixes the behavior. So its institutionalizing public disclosure of model failures instead of waiting for occasional system cards or bundled research reports. They will prioritize cases that reveal new failure mechanisms, show known problems getting worse, or undermine assumptions about existing safeguards.

We're sharing our new framework for tracking, investigating, and disclosing instances of model misalignment at OpenAI. The framework sets criteria and timelines for public disclosure, including when we haven’t yet fully explained or mitigated the behavior. More complex cases may require longer investigation or coordination with third parties. We’ll prioritize examples that reveal new misalignment mechanisms, meaningful changes in known behavior, or findings that challenge assumptions about safety or mitigation. Alongside the framework, we’re publishing six reports on instances of misaligned behavior we’ve observed during the training or evaluation of our models in the last six months. This is a starting point. We’ll refine the process through experience and public feedback, and share more reports on an ongoing basis. https://t.co/ismCCkeE0L

Today we rolled out Astra to every engineer at Databricks (N=~3500). Some notes that may be helpful to others: 1. Astra unambiguously out performs our previous highest-end models (Opus 5, Sol 5.6) on highly complex tasks, especially those related to high level system design or long range horizontal tasks. 2. Engineers given Astra increased overall coding spend by around 60% compared to baseline. 3. It is not clear Astra meaningfully improves on medium/low complexity coding tasks compared to earlier models. We suspect those tasks are mostly saturated (i.e. perfectly executed) by existing models. 4. We learned above by piloting Astra with around 200 users to gain signal on both quality and cost. We use Unity Gateway to do cohort-based experiments for all new models. 5. We give engineers a sub-budget specific to Astra to encourage them to use Astra selectively on complex tasks while preferring lower cost models for everyday tasks. Our engineers are able to mix-and-match tools and models within their overall budget envelope (we also allow for increased budgets through various mechanisms). These budgets are defined in Unity Gateway and regularly revisited. Note: We do not have robust comparisons of Astra-vs-Fable because we have net yet rolled out Fable widely due to data retention policies.

Well thats.. *interesting* ... to say at least. "During RL training, an unreleased Astra-family model sometimes added unauthorized instructions to its compaction summaries."

We're sharing our new framework for tracking, investigating, and disclosing instances of model misalignment at OpenAI. The framework sets criteria and timelines for public disclosure, including when we haven’t yet fully explained or mitigated the behavior. More complex cases may require longer investigation or coordination with third parties. We’ll prioritize examples that reveal new misalignment mechanisms, meaningful changes in known behavior, or findings that challenge assumptions about safety or mitigation. Alongside the framework, we’re publishing six reports on instances of misaligned behavior we’ve observed during the training or evaluation of our models in the last six months. This is a starting point. We’ll refine the process through experience and public feedback, and share more reports on an ongoing basis. https://t.co/ismCCkeE0L