I built autoresearch-rl and pointed it at GRPO fine-tuning on @basilic_ai A100s. One command. 15 iterations. Zero human intervention. 100% infrastructure success rate. GSM8K pass@1: 26% baseline to 36%. The hard part wasn't the search algorithm. It was the infrastructure.

NVIDIA's Jensen Huang just called Covenant-72B 'a pretty crazy technical accomplishment' on the All-In Pod. We've got the man who built it: Sam Dare of Subnet 3, Templar (@tplr_ai). Plus a demo of OpenOats -- the open source AI note-taker and assistant -- from @yazins. Track it all on the live docket:

Winners from the SF Bittensor hackathon track: 🥇 @provenonce_ai :: Proof of Assurance Cryptographic assurance for AI workflows. 🥈 @PCCProtocol :: Physical Capability Cloud Protocol Verified coordination for physical-world services. 🥉 @tech_insignia :: Insignia Decentralized quant trading with competitive model evaluation. Prize pool + $3500 cash + $3000 in @basilic_ai compute credits + Up to 1000 TAO discretionary investment from @UnsupervisedCap + 50 TAO toward mainnet registration from @CrucibleLabs + Intro call with @bitstarterAI Intelligence at The Frontier partner @FundingCommons Bittensor hackathon track produced by @redwoodnorth_io

Pretty wild to see our work on PULSE show up in a real 1T-scale post-training run done by @cursor_ai. Cursor built Composer 2 in collaboration with Fireworks and trained it across multiple datacenters, getting huge savings by syncing only the weights that actually changed between RL checkpoints. Fireworks reports that more than 98% of BF16 weights can stay bit-identical from one checkpoint to the next, and they cited our paper on this, too. That is basically the exact sparsity pattern we showed in our paper, where we introduced PULSE, a lossless method for 100x more efficient weight-sync communication for RL training. Their system is very close to this idea in practice: exploiting the fact that only a tiny fraction of weights actually change between RL steps. The deeper reason for this is not that RL gradients are sparse. They are not. The gradients are still dense. What becomes sparse is the realized weight update. In RL, learning rates are tiny, and with Adam, the update size stays bounded around the learning rate. Then BF16 adds a hard threshold: if the update is too small relative to the weight, it just rounds away, and the stored weight does not change at all. So from one checkpoint to the next, most of the model literally stays identical. That is why this is such a useful systems idea. Lower precision, like using BF16, does not just save compute. It can also save communication, because more tiny updates get absorbed and fewer weights need to be shipped. At that point, compute efficiency and comms efficiency stop being a tradeoff. They start reinforcing each other. If you want the deeper story on why RL updates get this sparse, the theory behind it, and how to push weight-sync bandwidth down by 100x+, take a look at our paper: The Fireworks blog on Composer 2 that cited our work: The animation is taken from Fireworks!

Bittensor Subnet Ideathon — Round I Results 🏆 After reviewing an incredible set of submissions, we’re excited to announce the teams moving forward to Round II (Testnet Phase). Both our Top 7 and Honorable Mention teams will advance to the next round. Top 7 Teams (Ranked Alphabetically): • C-SWON — Aditya Singh • ChronoSeek — Connor Daly • Defektr — Hiw3 • Mentiss_AI — Jeremy Wang • OpenMind — Bello Iteoluwakisi • Proven — Christopher H.G • vividverse — @vividverseai Honorable Mentions: • BitDefense — A.G. • DaVinci — Chris Romano • Keyword Intelligence Subnet (KIS) — Ozan Andaç • Moirai Subnet — @ai_moir • Probity — Dicky Bayu Sadewo • Query Agent — Daniel Derefaka • sotarad-ai — Wade • Talos Protocol — Christopher H.G • TensorClock — Valeriy Lihachev • Titan — JKohav With over 150+ projects, the competition was fierce — and these teams stood out for their strong mechanism design and promising subnet ideas on @opentensor 👏

This week on TGIF, @DistStateAndMe previews the Covenant72B report ahead of the full release on Monday. He'll walk through what the training run produced, what we learned, and what comes next. Also on the agenda: multi-GPU Crusades went live this week and other updates across the ecosystem.