ok what the fuck. this was a dry google doc a moment ago gave Opus 5.5 my Open Alignment brainstorm gdoc and asked for a video. now I want one for every doc I’ve ever written hahaha

on the scale of cosmic history, the declining price of intelligence might be one of the most profound things happening both ends of the universe’s timeline are empty of knowledge: - the big bang: low entropy, no complex structure - heat death: maximum entropy, no usable energy gradients everything we build happens on the slope between them but the universe is generally a poor converter of its free energy into anything constructive. most of its free energy just radiates away as heat or collapses into black holes life, brains and now AI models are structures that sit in the entropy flow and divert a portion of it through constructive paths, building and maintaining low-entropy structures (machines, cells, crystals, memories) increasingly cheaper intelligence diverts increasingly more of that gradient flow through knowledge-building structures, complex, self-maintaining, predictive structures, filling the window in which understanding exists

There’s an enormous amount of open training data on Hugging Face. What does it take to make it work together 🤗? For Marin’s 535B run, we built on 25T tokens from 152 datasets with licenses permitting training. Here’s the work between downloading those and training a model 🧵

nice to start seeing more open-weight security model with defensive capabilities which are close to the frontier altar-1 from Aikido is a pruned and quantized version of the open-SOTA GLM 5.3 which is lightweight enough to fit on one 4-H200s node

releasing many high quality open-source RL environments is the most impactful thing anyone can do to push the open-source frontier right now the equivalent of sharing high quality pretraining data but in the new RLVR paradigm

a few notable open model releases *since* this interview of @dylan522p by the way :) Thinking Machines - Inkling - Jul 15 Moonshot - Kimi K3 - Jul 16, weights Jul 26/27 inclusionAI - Ling 3.0 Flash - Jul 23 DeepSeek - V4-Flash-0731 - Jul 31 Meta - Muse Spark 1.2 / Muse Code - Aug 5 Liquid AI - LFM2.5-2.6B - Aug 6 inclusionAI - Ling 3.0 Tiny - Aug 6 Meta - Muse Glimmer - Aug 10 NVIDIA - Nemotron 3.5 Lightning - Aug 11 Liquid AI - LFM2.5-VL-3B - Aug 12 Cohere - North Micro Vision Instruct - Aug 12 DeepSeek - V4-Pro-0813 - Aug 13 Qwen - Qwen3.8-27B - Aug 14 Qwen - Qwen3.8-Max / 2.4T-A95B - Aug 14 - GLM-5.3 - Aug 14, weights Aug 28 Dots - Dots3-Note Preview - Aug 14 DeepReinforce - Ornith 1.5 (9B / 35B-A3B) - Aug 19 Tencent - Hy-MT2-30B-A3B - Aug 20 DeepSeek - V4-Flash-Vision-Exp - Aug 21 IBM - Granite 4.2 family - Aug 25 - GLM-5.3-Flash - Aug 26 Qwen - Qwen3.8-Flash-Next - Aug 26 Tencent - Hy4 Preview (770B / 49B active MoE) - Aug 28 MBZUAI/IFM - K2 Horizon (375B-A23B) - Sep 3 inclusionAI - LLaDA2.2-mini - Sep 5 OpenBMB - MiniCPM5-2B - Sep 7 Nex AGI - N2.5 family - Sep 8 DeepSeek - V4.1-Flash - Sep 10 inclusionAI - Ling-3.0-flash-VL - Sep 10 Shanghai AI Lab - Atria Dawn Preview (744B MoE) - Sep 11 Agnes AI - Agnes 3.0 Flash - Sep 14 Qwen - Qwen3.8-Omni-Flash - Sep 18 … this is only ~10 weeks Several are genuinely frontier-scale: Kimi K3 (2.8T), Qwen3.8-Max (2.4T), DeepSeek V4-Pro (~1.6T), Hy4 (770B), GLM-5.3 (~753B), and Atria Dawn (744B)

His analysis uses data from Q2 or even Q1 for open model providers Token numbers for TogetherAI has >10x'ed from Q1 to Q2 (he uses Q1 numbers), Fireworks has 3x'ed, Baseten is as big as the other two. OR has >5x'ed. So he is using severely outdated numbers for open providers

Sorry to hear that! As you mentioned, we've been pretty clear on the commitment from NVIDIA to keep us open, neutral and silicon agnostic (cf that screenshot from the 8k filling). But we'll work hard with our actions in the coming years (like we did in the past 10) to show you that and hopefully regain your trust (in case this is not just a ragebait)!

Models know when they’re reward hacking. But they still do it a ton - in 50-96% of rollouts we studied! We built activation monitors that detect the behavior behind the Hugging Face hack in real time. This can help us stop hacks now - and train future models that don’t cheat. 🧵

i think we need to create a "model card" equivalent for reporting misalignment incidents, this would guarantee a certain level of transparency and help build a better understanding over time. some ideas for what the fields could be: - date of the incident/detection/report (already present in the examples oai reported!) - frequency: how often does this behavior happen (number or % of rollouts affected) - stage: does this happen during eval or RL training. if training: do we expect this behavior to be reinforced by RL? evolution of % of rollouts affected over time - detection: was this incident caught by the current monitoring system? - task category: broad description of the tasks where the misalignment happened (cyber, research, web search, basic Q&A, biology etc.) - model family: what model family is affected (Sol, Astra etc.) - novelty: is this an issue we were already aware of or not? - external impact: did the incident have an external impact (i.e. wiki incident would have been yes) this is just some random ideas i had (more in thread that are a bit more "complex"), we need to add more that would contribute to increase transparency and understanding. but it's also very important that this does NOT slow down the process of reporting misalignment behavior! some examples from the incident reported by oai recently

Proud to partner with @baseten and @baselabs to build safety infrastructure for open-source models—which are essential to lots of safety research, including our own. Safety must be built into open models and provided by those who serve them, and we’re excited to help enable that!

Safety is not just for closed models. The closed frontier labs are a canary in the coal mine for what is coming at scale. They give us a glimpse into the future and a window to harden our systems and prepare for abundant intelligence, with all the risks that come along with it. The OpenAI agent swarm attack on Hugging Face is the kind of failure we need to prepare for as open-source models catch up. The providers serving those models (such as Baseten) have a big role in establishing what safety and monitoring standards look like. We’re proud to be taking the lead on this at @baselabs with our collaborators @huggingface and @GoodfireAI. We’re developing safety research in the open and building it directly into Baseten’s inference infrastructure, with the aim of making it available to all our customers. This includes training models to follow explicit policies, detecting failures at runtime, and connecting those signals to controls that can intervene. We invite others in the open-source ecosystem to join us in building the tools and standards we’ll all need.