I was wondering if OPAL could help the optimizer for the 0-order noise val track. So I took the (old 10M token) Noise benchmark's best validation-loss record (#14, 3.676) and wrapped its optimizer with OPAL. Every 50 updates, gpt-6.1-sol reads the loss and its own diagnostics, rewrites the update rule in Python, and tests it before it goes live. Results (seed 0): - record: 3.6758 - no agent (rerun): 3.6773 - agent, neutral prompt: 3.6791 - agent, prompt informed by the record: 3.6695 - same, xhigh reasoning: 3.6656 (−0.010 vs the record) The OPAL system prompt mattered: telling the agent what its gradients are, how the record's optimizer came about, and how the end-of-run weight merge works turned no gain into a clear one. There is an interesting moment at update 243: The record switches its gradient estimator (Boost → Cut), and the gradients change scale. All three agents had tuned their optimizers on Boost's gradients, and they lost ground right after the switch, up to 0.03 in training loss. OPAL noticed, rescaled the LM head's Adam statistics to the new gradients, and kept adjusting. By update ~620, every run was ahead of the record's optimizer again, and the informed runs stayed ahead to the end. What the best agent changed, keeping the record's ANVIL II update for the weight matrices: - per-layer rates for the residual mixing scalars, which it halved at the start - matrix rate −10%, +20% on two query matrices, −15% on the value matrices - longer momentum averaging (higher β1) for the scalars, head and gates - the head's Adam rescale at the estimator switch This is, of course, only anecdotal, and a minor improvement. Still, demonstrates that OPAL doesn't diverge and can mine small gains. Intuitively, online agent-guided adaptation makes the most sense when hyperparameters/configurations haven't been tuned to death.

Bro optimizer perhaps can adapt live. But don't think architecture can adapt mid training

1/9 Current optimizers are static during training. What if the optimizer could think and react while the network trains? We let an agent watch the training dynamics and rewrite the optimizer's source code as training goes: a reactive optimizer. In every language and image setting we tried that has no tuned recipe, it matches or beats the best recipe one can one-shot, and it guards against loss spikes and overfitting by itself. In the near future, optimization decisions may belong to agents, not humans. 📝 xingjianbai.com/reactive-opt… 💻 Try it on your task: github.com/xingjian-bai/opal 🧵

YES! i believe AGI can check every data every neuron every hyper-param in optimier per every step, and this is what the next 10x we can get from pretraining!

I gave GPT-6 Astra photos of things in my apartment to rebuild in Minecraft. The little fella walked, jumped, and built scaffolding to place blocks in survival mode. No flying or direct world edits. This was so fun to watch! More on this soon! With @SimulatedAnneal

Robot learning is moving beyond policies built for one robot, one scene, one task. At MIT, we’re exploring a different path: turning video world models into embodiment-agnostic robot policies. Introducing VERA: a 14B video-to-action system that controls robots across embodiments, skills, and environments. From zero-shot pick-and-place on a real Panda arm to contact-rich cube reorientation with a 16-DoF robotic hand. Different robots. Different environments. Different tasks. Same video planner. Same weights. We’re open-sourcing everything so you can fine-tune VERA for your own robot setup too. Deep dive in the thread: 🔗 vera.csail.mit.edu 🧵 (1/7)