I was actually enjoying my Saturday 😭

really well written blog on the emerging shift from optimization-limited to rollout-limited frontier RL. what stood out to me was the observation that as reasoning trajectories become longer and more heterogeneous GPU utilization and learner-generator synchronization increasingly dominate training efficiency. one underexplored question is whether capability jumps themselves can be detected through staleness statistics. if old trajectories suddenly become much less useful for learning that may indicate the model has discovered a qualitatively new reasoning strategy. in that sense replay-buffer value decay could become an observable signal of phase transitions in capability acquisition. if you're into async-RL/large-scale training systems this is a really worthwhile read.

The open-source protein ML space just got a massive upgrade. Phenomenal work by @anindyadeeps and @try_litefold on dropping the biggest protein data collection on Hugging Face

Check this out 👇

Damn — its soo over for open ai 🫡

The bitter lesson in 26 words: Don’t be distracted by human knowledge, as AI has been historically. Instead focus on methods for creating knowledge that scale with computation, like search and learning.

🚨Excited to announce Agent-BRACE! LLM agents in long-horizon POMDPs either blow up their context with raw history or summarize it, discarding uncertainty by collapsing belief into a point estimate. Agent-BRACE decouples the agent into belief state policy models, jointly trained via RL. Key takeaways: 1️⃣ 🎯The belief state model produces a structured approximation of the belief distribution as a set of atomic natural-language claims with ordinal verbalized certainty labels ranging from certain to unknown. The policy conditions on this compact belief rather than the full history. 2️⃣ 📈 Outperforms strong RL baselines on long-horizon partially observable embodied language environments while maintaining a near-constant context window independent of episode length. 3️⃣ 🔄 The learned belief becomes increasingly calibrated as evidence accumulates, and epistemic belief decreases over time: the proportion of claims that the agent has the strongest level of belief in grows from 21% → 52% over an episode. 👇🧵

Amazing talk by @DrJimFan -- exciting times for Physical AGI! TL;DR VLA architecture is parameter-misallocated toward language and should be replaced by World Action Models → pretrained video diffusion models that jointly predict future world states and robot actions, instantiated by Dream Zero (a 14B model running real-time control at 7Hz with 2× generalization gains over VLAs, @SeonghyeonYe ). arxiv.org/pdf/2602.15922 His central data claim is that egocentric human video is the FSD-equivalent ambient data flywheel for robotics, and EgoScale (@ruijie_zheng12) demonstrates a near-perfect log-linear scaling law (ℒ(N) = a − b log N, R² = 0.9983) between 1K and 20K hours of pretraining data and downstream dexterity performance. arxiv.org/pdf/2602.16710 His central environment claim is that classical physics simulators will be replaced by neural simulators, and Dream Dojo (@ShenyuanGao) demonstrates this with 44K hours of human video pretraining, 10 FPS real-time interaction, and Pearson r = 0.995 policy-evaluation fidelity. arxiv.org/pdf/2602.06949 Significant gaps in the talk: - it does not address runtime semantics (skill installation, behavior consistency, run-update separation) - it does not address the model-exploitation failure mode of training policies against learned simulators or learned rewards. My running notes w/ Opus 4.7 👇 docs.google.com/document/d/e… @NVIDIAAI

Notes on Robotics' End Game: Nvidia's Jim Fan! - @sidgraph and Opus 4.7 youtu.be/3Y8aq_ofEVs?si=aWEN… TL;DR Vision-Language-Action architecture is parameter-misallocated toward language and should be replaced by World Action Models → pretrained video diffusion models that jointly predict future world states and robot actions, instantiated by Dream Zero (a 14B model running real-time control at 7Hz with 2× generalization gains over VLAs). His central data claim is that egocentric human video is the FSD-equivalent ambient data flywheel for robotics, and EgoScale demonstrates a near-perfect log-linear scaling law (ℒ(N) = a − b log N, R² = 0.9983) between 1K and 20K hours of pretraining data and downstream dexterity performance. His central environment claim is that classical physics simulators will be replaced by neural simulators, and Dream Dojo demonstrates this with 44K hours of human video pretraining, 10 FPS real-time interaction, and Pearson r = 0.995 policy-evaluation fidelity. The framework is strongest on data (genuinely empirically grounded), partial on architecture (Dream Zero shows the substrate works but is GPT-2-stage, not GPT-3-stage), and weakest on the predictive claims about timeline and the "VLA is dead" rhetorical framing. Three significant gaps in the talk: it does not address runtime semantics (skill installation, behavior consistency, run-update separation), it does not address safety, and it does not address the model-exploitation failure mode of training policies against learned simulators or learned rewards. docs.google.com/document/d/e…

Annnddd its a house full 😄🥳 its soo fun to bring best researchers of blr together — conversations are sickk cool

Amazing weather to discuss science and computing 🫶

Vibe Coding is not vibing? Agents perform <60% on SWE-WebDevBench 👀. Code-level benchmarks (HumanEval, SWE-bench, FeatBench) take the specification as given and grade the patch. Vibe coding inverts this: the user gives natural-language intent, the platform must do PM, engineering, and ops in one pipeline. We evaluate across three orthogonal axes: ➡️ Interaction Mode (ACR=App Creation Request, vs AMR=App Modification Request), ➡️Agency Angle (PM × Engineering × Ops), ➡️Complexity Tier (T4 multi-role SaaS, T5 AI-native). Introducing SWE-WebDevBench: a comprehensive eval framework to assess AI coding platforms as virtual software development agencies, covering not just the middle step of coding, but the entire software lifecycle: Requirements gathering, planning, deployment and change management. @QwikBuild, @emergentlabs, @Lovable, @v0 see webdevbench.com 🧵